A Comprehensive Survey on Imbalanced Data Learning

Chong Chen; Conghui He; Dongting Xie; Hongzhi Yin; Wentao Zhang; Xinyi Gao; Yihang Zhang; Zhengren Wang

REVIEW 3 major objections 7 minor 290 references

A Comprehensive Survey on Imbalanced Data Learning

T0 review · 3 major / 7 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Imbalanced data has four levers, and none wins everywhere

desk verdict A broad and genuinely useful reference survey whose organizing taxonomy is fuzzier than claimed, with one flat-out wrong claim about micro F1 in the metrics section. read the letter →

arxiv 2502.08960 v3 pith:C3WCFOQL submitted 2025-02-13 cs.LG

Xinyi Gao , Dongting Xie , Yihang Zhang , Zhengren Wang , Chong Chen , Conghui He , Hongzhi Yin , Wentao Zhang This is my paper

classification cs.LG

keywords imbalanceddatalearningclassimbalancere-balancingfeaturerepresentationtrainingstrategyensemblelong-tailedrecognitionbenchmark

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This survey argues that the sprawling literature on imbalanced data learning can be understood through four method families—data re-balancing, feature representation, training strategy, and ensemble learning—each aligned with a phase of the machine-learning pipeline. It further claims that imbalance manifests differently in image, text, and graph data, so the right remedy depends on both the data format and the pipeline stage. To make that practical, the authors benchmark 20 representative methods on seven datasets and map the available open-source tooling. A sympathetic reader would take away that method choice is a design decision, not a single universal fix: simple synthetic oversampling and decoupled training are often strong, while deep generative models buy performance at very high computational cost.

What carries the argument

The load-bearing object is the taxonomy in Figure 1, which organizes methods into four categories with sub-branches—for example, data re-balancing splits into generation, adaptive under-sampling, hybrid sampling, and re-labeling, while feature representation splits into cost-sensitive learning, metric learning, supervised contrastive learning, prototype learning, transfer learning, and meta-learning. The taxonomy is designed to map onto the phases of the machine-learning process, so the survey can assign each method a motivation and a place to intervene. The second piece of machinery is a benchmark of 20 methods across seven datasets (ecoli, optical digits, pen digits, car eval, letter image, MNIST, FashionMNIST), which converts the taxonomy into a comparative map of what works where.

What would settle it

Check whether every method the survey itself describes can be assigned to exactly one of the four categories without stretching definitions. If a well-known method is naturally two categories at once—for example, a cost-sensitive bagging ensemble that also rebalances data—or falls outside all four, the roadmap's completeness claim fails. A concrete go-look test is to build a cross-reference table from Section II and count methods with multiple category memberships.

Watch

Extended reading notes

Core claim

The central claim is that every existing approach to learning from imbalanced data belongs to one of four categories that mirror the ML pipeline: manipulating the input distribution (data re-balancing), shaping the learned representation (feature representation), changing how training proceeds (training strategy), or combining models (ensemble learning). On the paper's own benchmark, no method dominates: SMOTE and SMOTEENN give the most robust conventional results, under-sampling improves minority recall at the cost of accuracy, and among deep methods transfer learning (FTL) ranks first while GAN generation ranks second at about 15 minutes versus roughly 2 seconds for classical augmentation. The survey also asserts that these imbalances are structural, not collection artifacts, and that each data format—images, text, graphs—requires format-specific adaptations such as node-degree and topology imbalance for graphs.

Load-bearing premise

The taxonomy's usefulness depends on the four categories being complete and separate, with no important method falling between or across them; this is asserted rather than proven.

Editorial extensions

If this is right

Researchers can use the taxonomy as a checklist: when a minority class is underperforming, first decide whether to intervene at the data, representation, training, or ensemble level.
On conventional tabular benchmarks, simple oversampling (SMOTE) and hybrid cleaning (SMOTEENN) are robust defaults, so expensive deep generation is not automatically justified.
Decoupled training—learning representations on imbalanced data, then fine-tuning a classifier on balanced data—offers a high-performance, low-cost option for deep learning.
GAN-based generation delivers top deep results but at two orders of magnitude higher augmentation cost, so cost-aware selection matters in deployment.
Format-specific imbalances, especially graph node degree and topology, require remedies that the four general categories alone do not fully specify.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The taxonomy's pipeline alignment implies a combinatorial design space: pairing a data re-balancing method with a training-strategy method could yield gains beyond any single category, a combination the survey notes but does not systematically test.
The benchmark's no-winner-everywhere result suggests that dataset-specific selection should itself be treated as a meta-learning problem; a method that predicts the best category from dataset statistics would be a natural extension.
For graph data, treating degree and topology imbalance as separate axes suggests the four-category scheme may need a fifth structural re-balancing axis for non-IID data.
LLM-based augmentation, which the survey highlights as a future direction, could be stress-tested by re-running the same seven-dataset benchmark with prompt-generated minority samples.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Useful survey, worth having on the shelf, but the headline framing oversells the taxonomy and one metric claim is flat-out wrong.

The genuine value: this synthesis covers classical and deep methods under one roof and actually takes text and graph data seriously, which the deep-long-tailed survey does not. The four-way organization (data re-balancing, feature representation, training strategy, ensemble) maps onto pipeline stages and works as a finding aid. The pseudo-code for the main algorithms is helpful; the classical benchmark (20 methods, 7 datasets) reports mean and standard deviation over ten runs, and the authors honestly note that no method wins everywhere. The graph section's split of node class/degree/topology imbalance is a real plus, and the resources section is practical.

Now the soft spots, in proportion.

First, Section IV's claim that micro F1 'emphasizes performance on minority classes' is backwards. Micro pooling is dominated by majority classes; macro F1 is the one weighting every class equally. Easy fix, but it has to be fixed.

Second, the taxonomy is not as 'distinct' as the abstract claims. Cost-sensitive learning sits under feature representation even though focal loss and class-balanced loss are training-time losses. Transfer learning appears in II-B5 and again as 'fine-tuning... also called transfer learning' in II-C2. Meta-learning swallows weights, samplers, and augmentation, mechanisms from three other categories in one bucket. This does not sink the survey: as a map, the four categories still route a reader to the right neighborhood. But the authors should tighten the boundaries or present the four as complementary lenses rather than distinct categories. The benchmark also does not validate the taxonomy; it is a method comparison, and the text should say so. The self-citations that exist (BMW-SMOTE, SNP-ECC, MLCC-IBC) are ordinary related-work mentions, not load-bearing.

Third, the deep-learning experiments omit architecture and hyperparameter details, so they are not reproducible as written. Minor: the tables say 'MINIST.'

Who gets value: practitioners choosing a technique, students entering the area, researchers wanting a compact benchmark table. It is not a paradigm shift and not the first survey, but a legitimate extension of He and Garcia and Krawczyk into the deep and graph eras.

Verdict: send to peer review. The micro-F1 error is a clean referee catch, the taxonomy framing needs honest revision, and the experimental section needs detail. All fixable; the paper deserves referee time.

Referee Report

3 major / 7 minor

Summary. This survey proposes a four-category taxonomy for imbalanced data learning—data re-balancing, feature representation, training strategy, and ensemble learning—and organizes a broad literature review around it. It covers imbalance in image, text, and graph data, reviews evaluation metrics, lists open-source libraries, and presents experiments comparing 12 classical and 10 deep-learning methods on seven benchmarks. The central claim is that this taxonomy and the accompanying benchmark provide a practical roadmap for researchers and practitioners.

Significance. If the taxonomy were cleanly defined, the survey would be a useful synthesis: it covers classical and deep methods, includes pseudocode for representative algorithms, addresses multiple data modalities, and adds a small benchmark with runtime comparisons. The coverage of generative models, graph-specific imbalance, and LLM-based augmentation is timely. However, the taxonomy's organizing value is weakened by internal overlaps that are not acknowledged, and the benchmark is under-specified. The paper ships no proofs or code, but the benchmark tables and pseudocode are helpful. The significance is therefore conditional on fixing the taxonomy's consistency and the metrics error.

major comments (3)

[Section II, Figure 1] The central claim that the four categories are distinct is not supported by the paper's own assignments. Cost-sensitive learning in Section II-B1 includes focal loss and class-balanced loss, which are training losses, yet Section II-C defines training strategies as mechanisms that 'adjust the learning process ... during model training,' which describes these losses equally well. Transfer learning appears both in Section II-B5 and again in Section II-C2, where fine-tuning is explicitly called transfer learning. Section II-B6 places meta-learning under feature representation, but the subsection includes weights, sampling methods, and data augmentation, which are cost-sensitive, data-rebalancing, and generation mechanisms. Section II-D4 similarly treats knowledge distillation as an ensemble method even though distillation is a training process. Because the four-way taxonomy is the paper's main contribution, these overlaps need to be addressed by refining the category definitions, explicitly discussing methods that span categories, or softening the claim of distinctness.
[Section IV, F1-score paragraph] The statement that micro-averaged F1 'is particularly useful in imbalanced datasets, as it emphasizes performance on minority classes' is incorrect. Micro-averaging aggregates true positives, false positives, and false negatives over all classes, so it is dominated by the largest classes; macro-averaging computes the metric per class and then averages, which is the standard way to give minority classes equal weight. This error is load-bearing in a section intended to guide practitioners in choosing metrics for imbalanced problems, and it should be corrected.
[Section V, Tables IV and V] The deep-learning benchmark is under-specified and cannot be reproduced as reported. The text states only that the encoder is 'a convolutional neural network, followed by a MLP classifier,' with no optimizer, learning rate, batch size, number of epochs, data augmentation, or hyperparameter-selection procedure. The ranking in Table V—for example, FTL at rank 1.50 and GAN at rank 2.50—therefore reflects a single unverifiable configuration rather than a reliable comparison. Since the benchmark is presented as practical evidence for choosing among the taxonomy's categories, the experimental setup needs to be documented in sufficient detail, or the claims should be scaled back accordingly.

minor comments (7)

[Tables II and IV] The dataset name 'MINIST' is a typo and should be 'MNIST' in both tables.
[Section VI] The text says 'as shown in Table 1' when referring to the open-source tools overview; the table is numbered Table VI.
[Section III-B2] There is a typo, 'imbalnced,' in the discussion of multi-class news classification.
[Section III-B2] The sentence beginning 'The sample of 437,948 news articles' should read 'The sample of 437,948 news articles is organized into...' for grammatical correctness.
[Section II-B1] The phrase 'Wan et al. [19] is a pioneering' should be 'Wan et al. [19] are pioneers' or 'is pioneering work.'
[Section V] The sentence 'Following the setup in [1]' cites He and Garcia (2009), which is not the usual source for the 10:1 MNIST imbalance setup; a more specific benchmark citation would be helpful.
[Equation (6)] The G-Mean formula has a formatting artifact ('G-Mean= p') and the square root symbol is missing; the equation should be typeset as the geometric mean of sensitivity and specificity.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the survey's taxonomy and benchmarks are descriptive, not derived from fitted parameters or self-referential uniqueness arguments; the few self-citations are non-load-bearing.

full rationale

This is a literature survey, not a derivation-based paper. Its central claim is a four-way taxonomy of imbalanced-learning methods (data re-balancing, feature representation, training strategy, ensemble learning). The taxonomy is asserted and illustrated in Figure 1, with methods assigned to categories based on the authors' reading of the literature; it is not derived from equations, fitted parameters, or any formal uniqueness theorem. The benchmark in Section V compares 20 representative methods, but the comparison is an empirical evaluation, not a prediction that is forced by construction from a fitted quantity. The self-citations that appear (e.g., refs. [66], [131], [132]) are used to describe specific methods (BMW-SMOTE, SNP-ECC, MLCC-IBC) that the authors themselves contributed; these citations are not used to justify the taxonomy, to forbid alternative organizations, or to supply a load-bearing premise. Even the skeptical observation that some methods span multiple categories (e.g., cost-sensitive losses also operating as training strategies, or transfer learning appearing in both Section II-B5 and Section II-C2) is a concern about the taxonomy's mutual exclusivity and descriptive adequacy, not circularity: the survey does not define a category in terms of the methods it then claims to organize, nor does it fit a parameter and call the result a prediction. Accordingly, the paper is self-contained as a review, and no circular step can be exhibited from its own text.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

No new entities or fitted parameters. The survey relies on the literature and its own taxonomy as the primary contribution.

assumptions (2)

domain assumption Imbalanced data distributions significantly degrade ML model performance
Foundational premise stated in Section I motivating the entire survey.
ad hoc to paper The four-category taxonomy (data re-balancing, feature representation, training strategy, ensemble learning) is exhaustive and captures the major approaches
Organizational claim of the paper, asserted in Section I and Figure 1, not proven against every possible method.

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Cite this review

Pith. "Pith review of A Comprehensive Survey on Imbalanced Data Learning." pith.science (2026). https://pith.science/paper/C3WCFOQL

@misc{pith2026250208960,
  author       = {Pith},
  title        = {Pith review of: A Comprehensive Survey on Imbalanced Data Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/C3WCFOQL}},
  note         = {Machine review of arXiv:2502.08960}
}

read the original abstract

With the expansion of data availability, machine learning (ML) has achieved remarkable breakthroughs in both academia and industry. However, imbalanced data distributions are prevalent in various types of raw data and severely hinder the performance of ML by biasing the decision-making processes. To deepen the understanding of imbalanced data and facilitate the related research and applications, this survey systematically analyzes various real-world data formats and concludes existing researches for different data formats into four distinct categories: data re-balancing, feature representation, training strategy, and ensemble learning. This structured analysis helps researchers comprehensively understand the pervasive nature of imbalance across diverse data formats, thereby paving a clearer path toward achieving specific research goals. We provide an overview of relevant open-source libraries, spotlight current challenges, and offer novel insights aimed at fostering future advancements in this critical area of study.

Figures

Figures reproduced from arXiv: 2502.08960 by the authors.

**Figure 1.** A taxonomy of imbalanced learning methods. (1) Data re-balancing methods focus on modifying the input data distribution to mitigate class imbalance [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

**Figure 2.** The data re-balancing methods. Adaptive down-sampling methods reduce the number of majority samples to balance the class distribution. Linear [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** The feature representation methods aim to obtain high-quality and [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: The training strategy exploits different stages of the training pipeline to [PITH_FULL_IMAGE:figures/full_fig_p016_4.png]

**Figure 5.** Figure 5: Ensemble methods combine multiple base models and leverage the [PITH_FULL_IMAGE:figures/full_fig_p018_5.png]

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Works this paper leans on

290 extracted references · 55 canonical work pages

[1]

Learning from imbalanced data,

H. He and E. A. Garcia, “Learning from imbalanced data,”IEEE Transactions on knowledge and data engineering, vol. 21, no. 9, pp. 1263–1284, 2009

2009
[2]

Learning from imbalanced data: open challenges and future directions,

B. Krawczyk, “Learning from imbalanced data: open challenges and future directions,”Progress in artificial intelligence, vol. 5, no. 4, pp. 221–232, 2016

2016
[3]

Learning from class-imbalanced data: Review of methods and applications,

G. Haixiang, L. Yijing, J. Shang, G. Mingyun, H. Yuanyue, and G. Bing, “Learning from class-imbalanced data: Review of methods and applications,”Expert systems with applications, vol. 73, pp. 220– 239, 2017

2017
[4]

Deep long-tailed learning: A survey,

Y . Zhang, B. Kang, B. Hooi, S. Yan, and J. Feng, “Deep long-tailed learning: A survey,”IEEE transactions on pattern analysis and machine intelligence, vol. 45, no. 9, pp. 10 795–10 816, 2023

2023
[5]

Smote: synthetic minority over-sampling technique,

N. V . Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “Smote: synthetic minority over-sampling technique,”Journal of ar- tificial intelligence research, vol. 16, pp. 321–357, 2002

2002
[6]

mixup: Beyond empirical risk minimization,

H. Zhang, M. Cisse, Y . N. Dauphin, and D. Lopez-Paz, “mixup: Beyond empirical risk minimization,”arXiv preprint arXiv:1710.09412, 2017

arXiv 2017
[7]

A distance-based over-sampling method for learning from imbalanced data sets

J. De La Calleja and O. Fuentes, “A distance-based over-sampling method for learning from imbalanced data sets.” inFLAIRS conference, 2007, pp. 634–635

2007
[8]

On the use of surround- ing neighbors for synthetic over-sampling of the minority class,

V . Garc ´ıa, J. S ´anchez, and R. Mollineda, “On the use of surround- ing neighbors for synthetic over-sampling of the minority class,” inProceedings of the 8th conference on Simulation, modelling and optimization, 2008, pp. 389–394

2008

Show all 290 references

[9]

Borderline-smote: a new over- sampling method in imbalanced data sets learning,

H. Han, W.-Y . Wang, and B.-H. Mao, “Borderline-smote: a new over- sampling method in imbalanced data sets learning,” inInternational conference on intelligent computing. Springer, 2005, pp. 878–887

2005
[10]

Adasyn: Adaptive synthetic sampling approach for imbalanced learning,

H. He, Y . Bai, E. A. Garcia, and S. Li, “Adasyn: Adaptive synthetic sampling approach for imbalanced learning,” in2008 IEEE interna- tional joint conference on neural networks (IEEE world congress on computational intelligence). IEEE, 2008, pp. 1322–1328

2008
[11]

Msmote: Improving classifica- tion performance when training data is imbalanced,

S. Hu, Y . Liang, L. Ma, and Y . He, “Msmote: Improving classifica- tion performance when training data is imbalanced,” in2009 second international workshop on computer science and engineering, vol. 2. IEEE, 2009, pp. 13–17. 28

2009
[12]

An empirical comparison and evaluation of minority oversampling techniques on a large number of imbalanced datasets,

G. Kov ´acs, “An empirical comparison and evaluation of minority oversampling techniques on a large number of imbalanced datasets,” Applied Soft Computing, vol. 83, p. 105662, 2019

2019
[13]

Remix: rebalanced mixup,

H.-P. Chou, S.-C. Chang, J.-Y . Pan, W. Wei, and D.-C. Juan, “Remix: rebalanced mixup,” inComputer Vision–ECCV 2020 Workshops: Glas- gow, UK, August 23–28, 2020, Proceedings, Part VI 16. Springer, 2020, pp. 95–110

2020
[14]

Mixboost: Synthetic oversampling using boosted mixup for handling extreme imbalance,

A. Kabra, A. Chopra, N. Puri, P. Badjatiya, S. Verma, P. Gupta, and B. Krishnamurthy, “Mixboost: Synthetic oversampling using boosted mixup for handling extreme imbalance,” in2020 IEEE International Conference on Data Mining (ICDM). IEEE, 2020, pp. 1082–1087

2020
[15]

Balanced- mixup for highly imbalanced medical image classification,

A. Galdran, G. Carneiro, and M. A. Gonz ´alez Ballester, “Balanced- mixup for highly imbalanced medical image classification,” inMedical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27– October 1, ...

2021
[16]

Label-occurrence-balanced mixup for long-tailed recognition,

S. Zhang, C. Chen, X. Zhang, and S. Peng, “Label-occurrence-balanced mixup for long-tailed recognition,” inICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 3224–3228

2022
[17]

Kernel-based smote for svm classification of imbalanced datasets,

J. Mathew, M. Luo, C. K. Pang, and H. L. Chan, “Kernel-based smote for svm classification of imbalanced datasets,” inIECON 2015-41ST annual conference of the IEEE industrial electronics society. IEEE, 2015, pp. 001 127–001 132

2015
[18]

Kerneladasyn: Kernel based adaptive synthetic data generation for imbalanced learning,

B. Tang and H. He, “Kerneladasyn: Kernel based adaptive synthetic data generation for imbalanced learning,” in2015 IEEE congress on evolutionary computation (CEC). IEEE, 2015, pp. 664–671

2015
[19]

Variational autoencoder based synthetic data generation for imbalanced learning,

Z. Wan, Y . Zhang, and H. He, “Variational autoencoder based synthetic data generation for imbalanced learning,” in2017 IEEE Symposium Series on Computational Intelligence (SSCI), 2017, pp. 1–7

2017
[20]

Generative adversarial net- works,

I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y . Bengio, “Generative adversarial net- works,”Communications of the ACM, vol. 63, no. 11, pp. 139–144, 2020

2020
[21]

Effective data generation for imbalanced learning using conditional generative adversarial networks,

G. Douzas and F. Bacao, “Effective data generation for imbalanced learning using conditional generative adversarial networks,”Expert Systems with Applications, vol. 91, pp. 464–471, 2018

2018
[22]

Conditional generative adversarial nets,

M. Mirza and S. Osindero, “Conditional generative adversarial nets,” arXiv preprint arXiv:1411.1784, 2014

2014 arXiv
[23]

Bagan: Data augmentation with balancing gan,

G. Mariani, F. Scheidegger, R. Istrate, C. Bekas, and C. Malossi, “Bagan: Data augmentation with balancing gan,”arXiv preprint arXiv:1803.09655, 2018

2018 arXiv
[24]

Supervised class distribution learning for gans-based imbalanced classification,

Z. Cai, X. Wang, M. Zhou, J. Xu, and L. Jing, “Supervised class distribution learning for gans-based imbalanced classification,” in2019 IEEE International Conference on Data Mining (ICDM). IEEE, 2019, pp. 41–50

2019
[25]

Rvgan-tl: A generative adversarial networks and transfer learning- based hybrid approach for imbalanced data classification,

H. Ding, Y . Sun, N. Huang, Z. Shen, Z. Wang, A. Iftekhar, and X. Cui, “Rvgan-tl: A generative adversarial networks and transfer learning- based hybrid approach for imbalanced data classification,”Information Sciences, vol. 629, pp. 184–203, 2023

2023
[26]

Wasserstein auto-encoders,

I. Tolstikhin, O. Bousquet, S. Gelly, and B. Schoelkopf, “Wasserstein auto-encoders,”arXiv preprint arXiv:1711.01558, 2017

2017 arXiv
[27]

Ewgan: Entropy-based wasserstein gan for imbalanced learning,

J. Ren, Y . Liu, and J. Liu, “Ewgan: Entropy-based wasserstein gan for imbalanced learning,” inProceedings of the AAAI conference on artificial intelligence, vol. 33, no. 01, 2019, pp. 10 011–10 012

2019
[28]

Wasserstein generative adversarial networks,

M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein generative adversarial networks,” inInternational conference on machine learning. PMLR, 2017, pp. 214–223

2017
[29]

Eid-gan: Generative adversarial nets for extremely imbalanced data augmentation,

W. Li, J. Chen, J. Cao, C. Ma, J. Wang, X. Cui, and P. Chen, “Eid-gan: Generative adversarial nets for extremely imbalanced data augmentation,”IEEE Transactions on Industrial Informatics, vol. 19, no. 3, pp. 3208–3218, 2022

2022
[30]

Smotified-gan for class imbal- anced pattern classification problems,

A. Sharma, P. Singh, and R. Chandra, “Smotified-gan for class imbal- anced pattern classification problems,”IEEE Access, vol. 10, pp. 1–1, 01 2022

2022
[31]

Generative adversarial minority oversampling,

S. S. Mullick, S. Datta, and S. Das, “Generative adversarial minority oversampling,” inProceedings of the IEEE/CVF International Confer- ence on Computer Vision (ICCV), October 2019

2019
[32]

Denoising diffusion probabilistic models,

J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,”Advances in neural information processing systems, vol. 33, pp. 6840–6851, 2020

2020
[33]

Semantic image synthesis via diffusion models,

W. Wang, J. Bao, W. Zhou, D. Chen, D. Chen, L. Yuan, and H. Li, “Semantic image synthesis via diffusion models,”arXiv preprint arXiv:2207.00050, 2022

2022
[34]

Diffmix: Diffusion model-based data synthesis for nuclei segmentation and classification in imbalanced pathology image datasets,

H.-J. Oh and W.-K. Jeong, “Diffmix: Diffusion model-based data synthesis for nuclei segmentation and classification in imbalanced pathology image datasets,”arXiv preprint arXiv:2306.14132, 2023

2023 arXiv
[35]

Dif- fusion augmentation for sequential recommendation,

Q. Liu, F. Yan, X. Zhao, Z. Du, H. Guo, R. Tang, and F. Tian, “Dif- fusion augmentation for sequential recommendation,”arXiv preprint arXiv:2309.12858, 2023

2023 arXiv
[36]

Mosaicfusion: Diffusion models as data augmenters for large vocabulary instance segmentation,

J. Xie, W. Li, X. Li, Z. Liu, Y . S. Ong, and C. C. Loy, “Mosaicfusion: Diffusion models as data augmenters for large vocabulary instance segmentation,”arXiv preprint arXiv:2309.13042, 2023

2023 arXiv
[37]

Pogdiff: Product-of- gaussians diffusion models for imbalanced text-to-image generation,

Z. Wang, S. Wei, X. Huo, and H. Wang, “Pogdiff: Product-of- gaussians diffusion models for imbalanced text-to-image generation,” arXiv preprint arXiv:2502.08106, 2025

2025 arXiv
[38]

Training class- imbalanced diffusion model via overlap optimization,

D. Yan, L. Qi, V . T. Hu, M.-H. Yang, and M. Tang, “Training class- imbalanced diffusion model via overlap optimization,”arXiv preprint arXiv:2402.10821, 2024

2024 arXiv
[39]

Latent-based diffusion model for long-tailed recognition,

H. Pengxiao, Y . Changkun, Z. Jieming, Z. Jing, J. Hong, and L. Xuesong, “Latent-based diffusion model for long-tailed recognition,” inThis CVPR Workshop paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to...

2024
[40]

Rethinking noise sampling in class-imbalanced diffusion models,

C. Xu, J. Yan, M. Yang, and C. Deng, “Rethinking noise sampling in class-imbalanced diffusion models,”IEEE Transactions on Image Processing, 2024

2024
[41]

Addressing the curse of imbalanced training sets: one-sided selection,

M. Kubat, S. Matwinet al., “Addressing the curse of imbalanced training sets: one-sided selection,” inIcml, vol. 97, no. 1. Citeseer, 1997, p. 179

1997
[42]

The condensed nearest neighbor rule (corresp.),

P. Hart, “The condensed nearest neighbor rule (corresp.),”IEEE trans- actions on information theory, vol. 14, no. 3, pp. 515–516, 1968

1968
[43]

An experiment with the edited nearest-neighbor rule,

I. Tomek, “An experiment with the edited nearest-neighbor rule,”IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-6, no. 6, pp. 448–452, 1976

1976
[44]

Improving identification of difficult small classes by balancing class distribution,

J. Laurikkala, “Improving identification of difficult small classes by balancing class distribution,” inConference on artificial intelligence in medicine in Europe. Springer, 2001, pp. 63–66

2001
[45]

Knn model-based approach in classification,

G. Guo, H. Wang, D. Bell, Y . Bi, and K. Greer, “Knn model-based approach in classification,” inOn The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE: OTM Confederated In- ternational Conferences, CoopIS, DOA, and ODBASE 2003, Catania, Sicily, Italy, Novembe...

2003
[46]

knn approach to unbalanced data distributions: a case study involving information extraction,

I. Mani and I. Zhang, “knn approach to unbalanced data distributions: a case study involving information extraction,” inProceedings of workshop on learning from imbalanced datasets, vol. 126. ICML, 2003, pp. 1–7

2003
[47]

Neighbourhood-based under- sampling approach for handling imbalanced and overlapped data,

P. Vuttipittayamongkol and E. Elyan, “Neighbourhood-based under- sampling approach for handling imbalanced and overlapped data,” Information Sciences, vol. 509, pp. 47–70, 2020

2020
[48]

Cluster-based under-sampling approaches for imbalanced data distributions,

S.-J. Yen and Y .-S. Lee, “Cluster-based under-sampling approaches for imbalanced data distributions,”Expert Systems with Applications, vol. 36, no. 3, pp. 5718–5727, 2009

2009
[49]

Cluster-based majority under-sampling approaches for class imbalance learning,

Y .-P. Zhang, L.-N. Zhang, and Y .-C. Wang, “Cluster-based majority under-sampling approaches for class imbalance learning,” in2010 2nd IEEE International Conference on Information and Financial Engineering. IEEE, 2010, pp. 400–404

2010
[50]

Clustering-based undersampling in class-imbalanced data,

W.-C. Lin, C.-F. Tsai, Y .-H. Hu, and J.-S. Jhang, “Clustering-based undersampling in class-imbalanced data,”Information Sciences, vol. 409, pp. 17–26, 2017

2017
[51]

Diversified sensitivity-based undersampling for imbalance classification problems,

W. W. Ng, J. Hu, D. S. Yeung, S. Yin, and F. Roli, “Diversified sensitivity-based undersampling for imbalance classification problems,” IEEE transactions on cybernetics, vol. 45, no. 11, pp. 2402–2412, 2014

2014
[52]

Fast-cbus: A fast clustering-based undersampling method for addressing the class im- balance problem,

N. Ofek, L. Rokach, R. Stern, and A. Shabtai, “Fast-cbus: A fast clustering-based undersampling method for addressing the class im- balance problem,”Neurocomputing, vol. 243, pp. 88–102, 2017

2017
[53]

Evolutionary undersampling for classifica- tion with imbalanced datasets: Proposals and taxonomy,

S. Garc ´ıa and F. Herrera, “Evolutionary undersampling for classifica- tion with imbalanced datasets: Proposals and taxonomy,”Evolutionary computation, vol. 17, no. 3, pp. 275–306, 2009

2009
[54]

Evolutionary undersampling for imbalanced big data classification,

I. Triguero, M. Galar, S. Vluymans, C. Cornelis, H. Bustince, F. Her- rera, and Y . Saeys, “Evolutionary undersampling for imbalanced big data classification,” in2015 IEEE Congress on Evolutionary Compu- tation (CEC). IEEE, 2015, pp. 715–722

2015
[55]

Eusc: A clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification,

H. L. Le, D. Landa-Silva, M. Galar, S. Garcia, and I. Triguero, “Eusc: A clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification,”Applied Soft Computing, vol. 101, p. 107033, 2021

2021
[56]

Eusboost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling,

M. Galar, A. Fern ´andez, E. Barrenechea, and F. Herrera, “Eusboost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling,”Pattern recognition, vol. 46, no. 12, pp. 3460–3471, 2013. 29

2013
[57]

Evolutionary undersampling boosting for imbalanced classification of breast cancer malignancy,

B. Krawczyk, M. Galar, Ł. Jele ´n, and F. Herrera, “Evolutionary undersampling boosting for imbalanced classification of breast cancer malignancy,”Applied Soft Computing, vol. 38, pp. 714–726, 2016

2016
[58]

Trainable undersampling for class-imbalance learning,

M. Peng, Q. Zhang, X. Xing, T. Gui, X. Huang, Y .-G. Jiang, K. Ding, and Z. Chen, “Trainable undersampling for class-imbalance learning,” inProceedings of the AAAI conference on artificial intelligence, vol. 33, no. 01, 2019, pp. 4707–4714

2019
[59]

Spatial distribution-based imbalanced undersampling,

Y . Yan, Y . Zhu, R. Liu, Y . Zhang, Y . Zhang, and L. Zhang, “Spatial distribution-based imbalanced undersampling,”IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 6, pp. 6376–6391, 2023

2023
[60]

Relevant information un- dersampling to support imbalanced data classification,

J. Hoyos-Osorio, A. Alvarez-Meza, G. Daza-Santacoloma, A. Orozco- Gutierrez, and G. Castellanos-Dominguez, “Relevant information un- dersampling to support imbalanced data classification,”Neurocomput- ing, vol. 436, pp. 136–146, 2021

2021
[61]

Entropy and confidence-based un- dersampling boosting random forests for imbalanced problems,

Z. Wang, C. Cao, and Y . Zhu, “Entropy and confidence-based un- dersampling boosting random forests for imbalanced problems,”IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 12, pp. 5178–5191, 2020

2020
[62]

A study of the behavior of several methods for balancing machine learning training data,

G. E. Batista, R. C. Prati, and M. C. Monard, “A study of the behavior of several methods for balancing machine learning training data,”ACM SIGKDD explorations newsletter, vol. 6, no. 1, pp. 20–29, 2004

2004
[63]

A cluster-based hybrid sampling approach for imbalanced data classification,

S. Feng, C. Zhao, and P. Fu, “A cluster-based hybrid sampling approach for imbalanced data classification,”Review of Scientific Instruments, vol. 91, no. 5, 2020

2020
[64]

Smote-rsb*: a hybrid preprocessing approach based on oversampling and undersam- pling for high imbalanced data-sets using smote and rough sets theory,

E. Ramentol, Y . Caballero, R. Bello, and F. Herrera, “Smote-rsb*: a hybrid preprocessing approach based on oversampling and undersam- pling for high imbalanced data-sets using smote and rough sets theory,” Knowledge and information systems, vol. 33, pp. 245–265, 2012

2012
[65]

Smote–ipf: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering,

J. A. S ´aez, J. Luengo, J. Stefanowski, and F. Herrera, “Smote–ipf: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering,”Information Sciences, vol. 291, pp. 184–203, 2015

2015
[66]

An ensemble imbalanced classification method based on model dynamic selection driven by data partition hybrid sampling,

X. Gao, B. Ren, H. Zhang, B. Sun, J. Li, J. Xu, Y . He, and K. Li, “An ensemble imbalanced classification method based on model dynamic selection driven by data partition hybrid sampling,”Expert Systems with Applications, vol. 160, p. 113660, 2020

2020
[67]

Bbn: Bilateral-branch network with cumulative learning for long-tailed visual recognition,

B. Zhou, Q. Cui, X.-S. Wei, and Z.-M. Chen, “Bbn: Bilateral-branch network with cumulative learning for long-tailed visual recognition,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 9719–9728

2020
[68]

The devil is in classification: A simple framework for long- tail instance segmentation,

T. Wang, Y . Li, B. Kang, J. Li, J. Liew, S. Tang, S. Hoi, and J. Feng, “The devil is in classification: A simple framework for long- tail instance segmentation,” inComputer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XIV 16. ...

2020
[69]

Long-tailed multi-label visual recognition by collaborative training on uniform and re-balanced samplings,

H. Guo and S. Wang, “Long-tailed multi-label visual recognition by collaborative training on uniform and re-balanced samplings,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 15 089–15 098

2021
[70]

Overcoming classifier imbalance for long-tail object detection with balanced group softmax,

Y . Li, T. Wang, B. Kang, S. Tang, C. Wang, J. Li, and J. Feng, “Overcoming classifier imbalance for long-tail object detection with balanced group softmax,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 10 991–11 000

2020
[71]

Learning from multiple experts: Self- paced knowledge distillation for long-tailed classification,

L. Xiang, G. Ding, and J. Han, “Learning from multiple experts: Self- paced knowledge distillation for long-tailed classification,” inComputer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part V 16. Springer, 2020, pp. 247–263

2020
[72]

Ace: Ally complementary experts for solving long-tailed recognition in one-shot,

J. Cai, Y . Wang, and J.-N. Hwang, “Ace: Ally complementary experts for solving long-tailed recognition in one-shot,” inProceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 112–121

2021
[73]

Reslt: Residual learning for long-tailed recognition,

J. Cui, S. Liu, Z. Tian, Z. Zhong, and J. Jia, “Reslt: Residual learning for long-tailed recognition,”IEEE transactions on pattern analysis and machine intelligence, vol. 45, no. 3, pp. 3695–3706, 2022

2022
[74]

Self-supervised aggregation of diverse experts for test-agnostic long-tailed recognition,

Y . Zhang, B. Hooi, L. Hong, and J. Feng, “Self-supervised aggregation of diverse experts for test-agnostic long-tailed recognition,”Advances in Neural Information Processing Systems, vol. 35, pp. 34 077–34 090, 2022

2022
[75]

Potential anchoring for imbalanced data classification,

M. Koziarski, “Potential anchoring for imbalanced data classification,” Pattern Recognition, vol. 120, p. 108114, 2021

2021
[76]

Constructing balance from imbal- ance for long-tailed image recognition,

Y . Xu, Y .-L. Li, J. Li, and C. Lu, “Constructing balance from imbal- ance for long-tailed image recognition,” inEuropean Conference on Computer Vision. Springer, 2022, pp. 38–56

2022
[77]

Ehso: Evolutionary hybrid sampling in overlapping scenarios for imbalanced learning,

Y . Zhu, Y . Yan, Y . Zhang, and Y . Zhang, “Ehso: Evolutionary hybrid sampling in overlapping scenarios for imbalanced learning,”Neuro- computing, vol. 417, pp. 333–346, 2020

2020
[78]

Dynamic sampling in convolutional neural networks for imbalanced data clas- sification,

S. Pouyanfar, Y . Tao, A. Mohan, H. Tian, A. S. Kaseb, K. Gauen, R. Dailey, S. Aghajanzadeh, Y .-H. Lu, S.-C. Chenet al., “Dynamic sampling in convolutional neural networks for imbalanced data clas- sification,” in2018 IEEE conference on multimedia information pro- cessing and...

2018
[79]

Rethinking the value of labels for improving class-imbalanced learning,

Y . Yang and Z. Xu, “Rethinking the value of labels for improving class-imbalanced learning,”Advances in neural information processing systems, vol. 33, pp. 19 290–19 301, 2020

2020
[80]

Crest: A class- rebalancing self-training framework for imbalanced semi-supervised learning,

C. Wei, K. Sohn, C. Mellina, A. Yuille, and F. Yang, “Crest: A class- rebalancing self-training framework for imbalanced semi-supervised learning,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 10 857–10 866

2021
[81]

Sar: Self-adaptive refinement on pseudo labels for multiclass-imbalanced semi-supervised learning,

Z. Lai, C. Wang, S.-c. Cheung, and C.-N. Chuah, “Sar: Self-adaptive refinement on pseudo labels for multiclass-imbalanced semi-supervised learning,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2022, pp. 4091–4100

2022
[82]

Daso: Distribution-aware semantics-oriented pseudo-label for imbalanced semi-supervised learn- ing,

Y . Oh, D.-J. Kim, and I. S. Kweon, “Daso: Distribution-aware semantics-oriented pseudo-label for imbalanced semi-supervised learn- ing,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 9786–9796

2022
[83]

Active learning for class imbalance problem,

S. Ertekin, J. Huang, and C. L. Giles, “Active learning for class imbalance problem,” inProceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, 2007, pp. 823–824

2007
[84]

Active learning with extreme learning machine for online imbalanced multiclass classifica- tion,

J. Qin, C. Wang, Q. Zou, Y . Sun, and B. Chen, “Active learning with extreme learning machine for online imbalanced multiclass classifica- tion,”Knowledge-Based Systems, vol. 231, p. 107385, 2021

2021
[85]

Active learning for word sense disambiguation with methods for addressing the class imbalance problem,

J. Zhu and E. Hovy, “Active learning for word sense disambiguation with methods for addressing the class imbalance problem,” inProceed- ings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNL...

2007
[86]

Minority class oriented active learning for imbalanced datasets,

U. Aggarwal, A. Popescu, and C. Hudelot, “Minority class oriented active learning for imbalanced datasets,” in2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021, pp. 9920– 9927

2021
[87]

A comprehensive active learning method for multiclass imbalanced data streams with concept drift,

W. Liu, H. Zhang, Z. Ding, Q. Liu, and C. Zhu, “A comprehensive active learning method for multiclass imbalanced data streams with concept drift,”Knowledge-Based Systems, vol. 215, p. 106778, 2021

2021
[88]

A cost-sensitive active learning for imbalance data with uncertainty and diversity combination,

H. Dong, B. Zhu, and J. Zhang, “A cost-sensitive active learning for imbalance data with uncertainty and diversity combination,” in Proceedings of the 2020 12th International Conference on Machine Learning and Computing, 2020, pp. 218–224

2020
[89]

Deep active learning models for imbalanced image classification,

Q. Jin, M. Yuan, H. Wang, M. Wang, and Z. Song, “Deep active learning models for imbalanced image classification,”Knowledge- Based Systems, vol. 257, p. 109817, 2022

2022
[90]

Certainty-enhanced active learning for improv- ing imbalanced data classification,

J. H. Fu and S. L. Lee, “Certainty-enhanced active learning for improv- ing imbalanced data classification,” in2011 IEEE 11th International Conference on Data Mining Workshops. IEEE, 2011, pp. 405–412

2011
[91]

Similarity-based active learning for image classification under class imbalance,

C. Zhang, W. Tavanapong, G. Kijkul, J. Wong, P. C. de Groen, and J. Oh, “Similarity-based active learning for image classification under class imbalance,” in2018 IEEE International Conference on Data Mining (ICDM), 2018, pp. 1422–1427

2018
[92]

Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm,

P. D. Turney, “Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm,”Journal of artificial intelligence research, vol. 2, pp. 369–409, 1994

1994
[93]

Thresholding for making classifiers cost- sensitive,

V . S. Sheng and C. X. Ling, “Thresholding for making classifiers cost- sensitive,” inAaai, vol. 6, 2006, pp. 476–81

2006
[94]

Metacost: A general method for making classifiers cost- sensitive,

P. Domingos, “Metacost: A general method for making classifiers cost- sensitive,” inProceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, 1999, pp. 155– 164

1999
[95]

Training cost-sensitive neural networks with methods addressing the class imbalance problem,

Z.-H. Zhou and X.-Y . Liu, “Training cost-sensitive neural networks with methods addressing the class imbalance problem,”IEEE Trans- actions on knowledge and data engineering, vol. 18, no. 1, pp. 63–77, 2005

2005
[96]

On multi-class cost-sensitive learning,

——, “On multi-class cost-sensitive learning,”Computational Intelli- gence, vol. 26, no. 3, pp. 232–257, 2010

2010
[97]

Novel cost-sensitive approach to improve the multilayer perceptron performance on imbalanced data,

C. L. Castro and A. P. Braga, “Novel cost-sensitive approach to improve the multilayer perceptron performance on imbalanced data,”IEEE transactions on neural networks and learning systems, vol. 24, no. 6, pp. 888–899, 2013

2013
[98]

Class-balanced loss based on effective number of samples,

Y . Cui, M. Jia, T.-Y . Lin, Y . Song, and S. Belongie, “Class-balanced loss based on effective number of samples,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 9268–9277. 30

2019
[99]

Focal loss for dense object detection,

T.-Y . Lin, P. Goyal, R. Girshick, K. He, and P. Doll ´ar, “Focal loss for dense object detection,” inProceedings of the IEEE international conference on computer vision, 2017, pp. 2980–2988

2017
[100]

Influence-balanced loss for imbalanced visual classification,

S. Park, J. Lim, Y . Jeon, and J. Y . Choi, “Influence-balanced loss for imbalanced visual classification,” inProceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 735–744

2021
[101]

Balanced meta-softmax for long-tailed visual recognition,

J. Ren, C. Yu, X. Ma, H. Zhao, S. Yiet al., “Balanced meta-softmax for long-tailed visual recognition,”Advances in neural information processing systems, vol. 33, pp. 4175–4186, 2020

2020
[102]

A novel cost sensitive neural network ensemble for multiclass imbalance data learning,

P. Cao, B. Li, D. Zhao, and O. Zaiane, “A novel cost sensitive neural network ensemble for multiclass imbalance data learning,” inThe 2013 International Joint Conference on Neural Networks (IJCNN). IEEE, 2013, pp. 1–8

2013
[103]

A cost-sensitive deep belief network for imbalanced classification,

C. Zhang, K. C. Tan, H. Li, and G. S. Hong, “A cost-sensitive deep belief network for imbalanced classification,”IEEE transactions on neural networks and learning systems, vol. 30, no. 1, pp. 109–122, 2018

2018
[104]

Generalized dataweighting via class-level gradient manipulation,

C. Chen, S. Zheng, X. Chen, E. Dong, X. S. Liu, H. Liu, and D. Dou, “Generalized dataweighting via class-level gradient manipulation,”Ad- vances in Neural Information Processing Systems, vol. 34, pp. 14 097– 14 109, 2021

2021
[105]

Equalization loss for long-tailed object recognition,

J. Tan, C. Wang, B. Li, Q. Li, W. Ouyang, C. Yin, and J. Yan, “Equalization loss for long-tailed object recognition,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 11 662–11 671

2020
[106]

Equalization loss v2: A new gradient balance approach for long-tailed object detection,

J. Tan, X. Lu, G. Zhang, C. Yin, and Q. Li, “Equalization loss v2: A new gradient balance approach for long-tailed object detection,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 1685–1694

2021
[107]

Learning to re- weight examples with optimal transport for imbalanced classification,

D. Guo, Z. Li, H. Zhao, M. Zhou, H. Zhaet al., “Learning to re- weight examples with optimal transport for imbalanced classification,” Advances in Neural Information Processing Systems, vol. 35, pp. 25 517–25 530, 2022

2022
[108]

Learning to reweight examples for robust deep learning,

M. Ren, W. Zeng, B. Yang, and R. Urtasun, “Learning to reweight examples for robust deep learning,” inInternational conference on machine learning. PMLR, 2018, pp. 4334–4343

2018
[109]

Meta-weight-net: Learning an explicit mapping for sample weighting,

J. Shu, Q. Xie, L. Yi, Q. Zhao, S. Zhou, Z. Xu, and D. Meng, “Meta-weight-net: Learning an explicit mapping for sample weighting,” Advances in neural information processing systems, vol. 32, 2019

2019
[110]

Deep localized metric learning,

Y . Duan, J. Lu, J. Feng, and J. Zhou, “Deep localized metric learning,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 28, no. 10, pp. 2644–2656, 2017

2017
[111]

Learning deep representation for imbalanced classification,

C. Huang, Y . Li, C. C. Loy, and X. Tang, “Learning deep representation for imbalanced classification,” inProceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 5375–5384

2016
[112]

Class rectification hard mining for imbalanced deep learning,

Q. Dong, S. Gong, and X. Zhu, “Class rectification hard mining for imbalanced deep learning,” inProceedings of the IEEE international conference on computer vision, 2017, pp. 1851–1860

2017
[113]

Iterative metric learning for imbalance data classification,

N. Wang, X. Zhao, Y . Jiang, Y . Gao, and K. BNRist, “Iterative metric learning for imbalance data classification,” inIJCAI, vol. 2018, 2018, pp. 2805–2811

2018
[114]

A quadruplet deep metric learning model for imbalanced time-series fault diagnosis,

X. Gui, J. Zhang, J. Tang, H. Xu, J. Zou, and S. Fan, “A quadruplet deep metric learning model for imbalanced time-series fault diagnosis,” Knowledge-Based Systems, vol. 238, p. 107932, 2022

2022
[115]

Borderline-margin loss based deep metric learning framework for imbalanced data,

M. Yan and N. Li, “Borderline-margin loss based deep metric learning framework for imbalanced data,”Applied Intelligence, vol. 53, no. 2, pp. 1487–1504, 2023

2023
[116]

Person re- identification by multi-channel parts-based cnn with improved triplet loss function,

D. Cheng, Y . Gong, S. Zhou, J. Wang, and N. Zheng, “Person re- identification by multi-channel parts-based cnn with improved triplet loss function,” inProceedings of the iEEE conference on computer vision and pattern recognition, 2016, pp. 1335–1344

2016
[117]

Range loss for deep face recognition with long-tailed training data,

X. Zhang, Z. Fang, Y . Wen, Z. Li, and Y . Qiao, “Range loss for deep face recognition with long-tailed training data,” inProceedings of the IEEE International Conference on Computer Vision, 2017, pp. 5409– 5418

2017
[118]

Learning a distance metric by balancing kl-divergence for imbalanced datasets,

L. Feng, H. Wang, B. Jin, H. Li, M. Xue, and L. Wang, “Learning a distance metric by balancing kl-divergence for imbalanced datasets,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 49, no. 12, pp. 2384–2395, 2018

2018
[119]

Gaussian affinity for max-margin class imbalanced learning,

M. Hayat, S. Khan, S. W. Zamir, J. Shen, and L. Shao, “Gaussian affinity for max-margin class imbalanced learning,” inProceedings of the IEEE/CVF international conference on computer vision, 2019, pp. 6469–6479

2019
[120]

Metric learning from imbalanced data with generalization guarantees,

L. Gautheron, A. Habrard, E. Morvant, and M. Sebban, “Metric learning from imbalanced data with generalization guarantees,”Pattern Recognition Letters, vol. 133, pp. 298–304, 2020

2020
[121]

Supervised contrastive learning,

P. Khosla, P. Teterwak, C. Wang, A. Sarna, Y . Tian, P. Isola, A. Maschinot, C. Liu, and D. Krishnan, “Supervised contrastive learning,”Advances in neural information processing systems, vol. 33, pp. 18 661–18 673, 2020

2020
[122]

Distance metric learning for large margin nearest neighbor classification

K. Q. Weinberger and L. K. Saul, “Distance metric learning for large margin nearest neighbor classification.”Journal of machine learning research, vol. 10, no. 2, 2009

2009
[123]

Improved deep metric learning with multi-class n-pair loss objective,

K. Sohn, “Improved deep metric learning with multi-class n-pair loss objective,”Advances in neural information processing systems, vol. 29, 2016

2016
[124]

Contrastive multiview coding,

Y . Tian, D. Krishnan, and P. Isola, “Contrastive multiview coding,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XI 16. Springer, 2020, pp. 776–794

2020
[125]

Exploring balanced feature spaces for representation learning,

B. Kang, Y . Li, S. Xie, Z. Yuan, and J. Feng, “Exploring balanced feature spaces for representation learning,” inInternational Conference on Learning Representations, 2021

2021
[126]

A class-aware supervised contrastive learning framework for imbalanced fault diagnosis,

J. Zhang, J. Zou, Z. Su, J. Tang, Y . Kang, H. Xu, Z. Liu, and S. Fan, “A class-aware supervised contrastive learning framework for imbalanced fault diagnosis,”Knowledge-Based Systems, vol. 252, p. 109437, 2022

2022
[127]

Guided point contrastive learning for semi-supervised point cloud semantic segmentation,

L. Jiang, S. Shi, Z. Tian, X. Lai, S. Liu, C.-W. Fu, and J. Jia, “Guided point contrastive learning for semi-supervised point cloud semantic segmentation,” inProceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 6423–6432

2021
[128]

Parametric contrastive learning,

J. Cui, Z. Zhong, S. Liu, B. Yu, and J. Jia, “Parametric contrastive learning,” inProceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 715–724

2021
[129]

Balanced contrastive learning for long-tailed visual recognition,

J. Zhu, Z. Wang, J. Chen, Y .-P. P. Chen, and Y .-G. Jiang, “Balanced contrastive learning for long-tailed visual recognition,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog- nition, 2022, pp. 6908–6917

2022
[130]

Targeted supervised contrastive learning for long-tailed recognition,

T. Li, P. Cao, Y . Yuan, L. Fan, Y . Yang, R. S. Feris, P. Indyk, and D. Katabi, “Targeted supervised contrastive learning for long-tailed recognition,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 6918–6928

2022
[131]

An ensemble contrastive classification framework for imbalanced learning with sample-neighbors pair construction,

X. Gao, X. Jia, J. Liu, B. Xue, Z. Huang, S. Fu, G. Zhang, and K. Li, “An ensemble contrastive classification framework for imbalanced learning with sample-neighbors pair construction,”Knowledge-Based Systems, vol. 249, p. 109007, 2022

2022
[132]

An imbalanced binary classification method based on contrastive learning using multi-label confidence comparisons within sample-neighbors pair,

X. Gao, Z. Meng, X. Jia, J. Liu, X. Diao, B. Xue, Z. Huang, and K. Li, “An imbalanced binary classification method based on contrastive learning using multi-label confidence comparisons within sample-neighbors pair,”Neurocomputing, vol. 517, pp. 148–164, 2023

2023
[133]

Proco: Prototype-aware contrastive learning for long-tailed medical image classification,

Z. Yang, J. Pan, Y . Yang, X. Shi, H.-Y . Zhou, Z. Zhang, and C. Bian, “Proco: Prototype-aware contrastive learning for long-tailed medical image classification,” inMedical Image Computing and Computer Assisted Intervention–MICCAI 2022: 25th International Conference, Singapore...

2022
[134]

Prototypical networks for few-shot learning,

J. Snell, K. Swersky, and R. Zemel, “Prototypical networks for few-shot learning,”Advances in neural information processing systems, vol. 30, 2017

2017
[135]

Contrastive learning based hybrid networks for long-tailed image classification,

P. Wang, K. Han, X.-S. Wei, L. Zhang, and L. Wang, “Contrastive learning based hybrid networks for long-tailed image classification,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 943–952

2021
[136]

Proaug: Prototype-based augmentation for long-tailed image classification,

Y . Hong, J. Zhang, Z. Sun, and K. Yan, “Proaug: Prototype-based augmentation for long-tailed image classification,” inICASSP 2024- 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024, pp. 3035–3039

2024
[137]

Large- scale long-tailed recognition in an open world,

Z. Liu, Z. Miao, X. Zhan, J. Wang, B. Gong, and S. X. Yu, “Large- scale long-tailed recognition in an open world,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 2537–2546

2019
[138]

Pan: Prototype-based adaptive network for robust cross-modal retrieval,

Z. Zeng, S. Wang, N. Xu, and W. Mao, “Pan: Prototype-based adaptive network for robust cross-modal retrieval,” inProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021, pp. 1125–1134

2021
[139]

Meta-prototype decoupled training for long-tailed learning,

S. Fu, H. Chu, X. He, H. Wang, Z. Yang, and H. Hu, “Meta-prototype decoupled training for long-tailed learning,” inProceedings of the Asian Conference on Computer Vision, 2022, pp. 569–585

2022
[140]

Inflated episodic memory with region self- attention for long-tailed visual recognition,

L. Zhu and Y . Yang, “Inflated episodic memory with region self- attention for long-tailed visual recognition,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 4344–4353

2020
[141]

Learning and memorizing representative prototypes for 3d point cloud semantic and instance 31 segmentation,

T. He, D. Gong, Z. Tian, and C. Shen, “Learning and memorizing representative prototypes for 3d point cloud semantic and instance 31 segmentation,” inComputer Vision–ECCV 2020: 16th European Con- ference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XVIII

2020
[142]

Springer, 2020, pp. 564–580

2020
[143]

Feature transfer learning for face recognition with under-represented data,

X. Yin, X. Yu, K. Sohn, X. Liu, and M. Chandraker, “Feature transfer learning for face recognition with under-represented data,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 5704–5713

2019
[144]

Deep representation learning on long-tailed data: A learnable embedding augmentation perspective,

J. Liu, Y . Sun, C. Han, Z. Dou, and W. Li, “Deep representation learning on long-tailed data: A learnable embedding augmentation perspective,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 2970–2979

2020
[145]

Feature space augmentation for long-tailed data,

P. Chu, X. Bian, S. Liu, and H. Ling, “Feature space augmentation for long-tailed data,” inComputer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16. Springer, 2020, pp. 694–710

2020
[146]

Rsg: A simple but effective module for learning imbalanced datasets,

J. Wang, T. Lukasiewicz, X. Hu, J. Cai, and Z. Xu, “Rsg: A simple but effective module for learning imbalanced datasets,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog- nition, 2021, pp. 3784–3793

2021
[147]

M2m: Imbalanced classification via major-to-minor translation,

J. Kim, J. Jeong, and J. Shin, “M2m: Imbalanced classification via major-to-minor translation,” inProceedings of the IEEE/CVF confer- ence on computer vision and pattern recognition, 2020, pp. 13 896– 13 905

2020
[148]

Safa: Sample-adaptive feature augmentation for long-tailed image classification,

Y . Hong, J. Zhang, Z. Sun, and K. Yan, “Safa: Sample-adaptive feature augmentation for long-tailed image classification,” inEuropean conference on computer vision. Springer, 2022, pp. 587–603

2022
[149]

Learning to model the tail,

Y .-X. Wang, D. Ramanan, and M. Hebert, “Learning to model the tail,” Advances in neural information processing systems, vol. 30, 2017

2017
[150]

Gistnet: a geometric structure transfer network for long-tailed recognition,

B. Liu, H. Li, H. Kang, G. Hua, and N. Vasconcelos, “Gistnet: a geometric structure transfer network for long-tailed recognition,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 8209–8218

2021
[151]

Boosting for transfer learning,

W. Dai, Q. Yang, G.-R. Xue, and Y . Yu, “Boosting for transfer learning,” inProceedings of the 24th International Conference on Machine Learning, ser. ICML ’07. New York, NY , USA: Association for Computing Machinery, 2007, p. 193–200

2007
[152]

Learning to balance: Bayesian meta-learning for imbalanced and out- of-distribution tasks,

H. B. Lee, H. Lee, D. Na, S. Kim, M. Park, E. Yang, and S. J. Hwang, “Learning to balance: Bayesian meta-learning for imbalanced and out- of-distribution tasks,” inICLR, 2020

2020
[153]

Re- thinking class-balanced methods for long-tailed visual recognition from a domain adaptation perspective,

M. A. Jamal, M. Brown, M.-H. Yang, L. Wang, and B. Gong, “Re- thinking class-balanced methods for long-tailed visual recognition from a domain adaptation perspective,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 7610–7619

2020
[154]

Learning fast sample re-weighting without re- ward data,

Z. Zhang and T. Pfister, “Learning fast sample re-weighting without re- ward data,” inProceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 725–734

2021
[155]

Improving the accuracy of learning exam- ple weights for imbalance classification,

Y . Liu, B. Cao, and J. Fan, “Improving the accuracy of learning exam- ple weights for imbalance classification,” inInternational Conference on Learning Representations, 2021

2021
[156]

Mesa: boost ensemble imbalanced learning with meta-sampler,

Z. Liu, P. Wei, J. Jiang, W. Cao, J. Bian, and Y . Chang, “Mesa: boost ensemble imbalanced learning with meta-sampler,”Advances in neural information processing systems, vol. 33, pp. 14 463–14 474, 2020

2020
[157]

Metasaug: Meta semantic augmentation for long-tailed visual recognition,

S. Li, K. Gong, C. H. Liu, Y . Wang, F. Qiao, and X. Cheng, “Metasaug: Meta semantic augmentation for long-tailed visual recognition,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 5212–5221

2021
[158]

Implicit semantic data augmentation for deep networks,

Y . Wang, X. Pan, S. Song, H. Zhang, G. Huang, and C. Wu, “Implicit semantic data augmentation for deep networks,”Advances in Neural Information Processing Systems, vol. 32, 2019

2019
[159]

Decoupling representation and classifier for long-tailed recognition,

B. Kang, S. Xie, M. Rohrbach, Z. Yan, A. Gordo, J. Feng, and Y . Kalantidis, “Decoupling representation and classifier for long-tailed recognition,”arXiv preprint arXiv:1910.09217, 2019

1910 arXiv
[160]

Learning imbal- anced datasets with label-distribution-aware margin loss,

K. Cao, C. Wei, A. Gaidon, N. Arechiga, and T. Ma, “Learning imbal- anced datasets with label-distribution-aware margin loss,”Advances in neural information processing systems, vol. 32, 2019

2019
[161]

A comprehensive frame- work for long-tailed learning via pretraining and normalization,

N. Kang, H. Chang, B. Ma, and S. Shan, “A comprehensive frame- work for long-tailed learning via pretraining and normalization,”IEEE Transactions on Neural Networks and Learning Systems, 2022

2022
[162]

Rethinking classifier re-training in long-tailed recognition: Label over- smooth can balance,

S. Sun, H. Lu, J. Li, Y . Xie, T. Li, X. Yang, L. Zhang, and J. Yan, “Rethinking classifier re-training in long-tailed recognition: Label over- smooth can balance,” inThe Thirteenth International Conference on Learning Representations, 2025

2025
[163]

Self-supervised learning is more robust to dataset imbalance,

H. Liu, J. Z. HaoChen, A. Gaidon, and T. Ma, “Self-supervised learning is more robust to dataset imbalance,”arXiv preprint arXiv:2110.05025, 2021

2021 arXiv
[164]

Self supervision to distillation for long- tailed visual recognition,

T. Li, L. Wang, and G. Wu, “Self supervision to distillation for long- tailed visual recognition,” inProceedings of the IEEE/CVF interna- tional conference on computer vision, 2021, pp. 630–639

2021
[165]

Using pre-training can im- prove model robustness and uncertainty,

D. Hendrycks, K. Lee, and M. Mazeika, “Using pre-training can im- prove model robustness and uncertainty,” inInternational Conference on Machine Learning. PMLR, 2019, pp. 2712–2721

2019
[166]

Factors in finetun- ing deep model for object detection with long-tail distribution,

W. Ouyang, X. Wang, C. Zhang, and X. Yang, “Factors in finetun- ing deep model for object detection with long-tail distribution,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 864–873

2016
[167]

Large scale fine-grained categorization and domain-specific transfer learning,

Y . Cui, Y . Song, C. Sun, A. Howard, and S. Belongie, “Large scale fine-grained categorization and domain-specific transfer learning,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 4109–4118

2018
[168]

Imbalanced breast cancer classification using transfer learning,

R. Singh, T. Ahmed, A. Kumar, A. K. Singh, A. K. Pandey, and S. K. Singh, “Imbalanced breast cancer classification using transfer learning,” IEEE/ACM transactions on computational biology and bioinformatics, vol. 18, no. 1, pp. 83–93, 2020

2020
[169]

Curriculum learning,

Y . Bengio, J. Louradour, R. Collobert, and J. Weston, “Curriculum learning,” inProceedings of the 26th annual international conference on machine learning, 2009, pp. 41–48

2009
[170]

Cased: curriculum adaptive sampling for extreme data imbalance,

A. Jesson, N. Guizard, S. H. Ghalehjegh, D. Goblot, F. Soudan, and N. Chapados, “Cased: curriculum adaptive sampling for extreme data imbalance,” inMedical Image Computing and Computer Assisted Intervention- MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, ...

2017
[171]

Curriculumnet: Weakly supervised learning from large- scale web images,

S. Guo, W. Huang, H. Zhang, C. Zhuang, D. Dong, M. R. Scott, and D. Huang, “Curriculumnet: Weakly supervised learning from large- scale web images,” inProceedings of the European conference on computer vision (ECCV), 2018, pp. 135–150

2018
[172]

Dynamic curriculum learning for imbalanced data classification,

Y . Wang, W. Gan, J. Yang, W. Wu, and J. Yan, “Dynamic curriculum learning for imbalanced data classification,” inProceedings of the IEEE/CVF international conference on computer vision, 2019, pp. 5017–5026

2019
[173]

Diagnosing glaucoma on imbalanced data with self-ensemble dual-curriculum learning,

R. Zhao, X. Chen, Z. Chen, and S. Li, “Diagnosing glaucoma on imbalanced data with self-ensemble dual-curriculum learning,”Medical Image Analysis, vol. 75, p. 102295, 2022

2022
[174]

Class probability estimates are unreliable for imbalanced data (and how to fix them),

B. C. Wallace and I. J. Dahabreh, “Class probability estimates are unreliable for imbalanced data (and how to fix them),” in2012 IEEE 12th international conference on data mining. IEEE, 2012, pp. 695– 704

2012
[175]

Distribution alignment: A unified framework for long-tail visual recognition

S. Zhang, Z. Li, S. Yan, X. He, and J. Sun, “Distribution alignment: A unified framework for long-tail visual recognition.” inCVPR, 2021

2021
[176]

Long-tailed visual recognition via gaussian clouded logit adjustment,

M. Li, Y .-m. Cheung, and Y . Lu, “Long-tailed visual recognition via gaussian clouded logit adjustment,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2022, pp. 6929–6938

2022
[177]

Dis- entangling label distribution for long-tailed visual recognition,

Y . Hong, S. Han, K. Choi, S. Seo, B. Kim, and B. Chang, “Dis- entangling label distribution for long-tailed visual recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2021, pp. 6626–6636

2021
[178]

Long-tail learning via logit adjustment,

A. K. Menon, S. Jayasumana, A. S. Rawat, H. J. andAndreas Veit, and S. Kumar, “Long-tail learning via logit adjustment,” in9th International Conference on Learning Representations (ICLR), 2021

2021
[179]

Identifying and compensating for feature deviation in imbalanced deep learning,

H.-J. Ye, H.-Y . Chen, D.-C. Zhan, and W.-L. Chao, “Identifying and compensating for feature deviation in imbalanced deep learning,”arXiv preprint arXiv:2001.01385, 2020

2001 arXiv
[180]

Posterior re- calibration for imbalanced datasets,

J. Tian, Y .-C. Liu, N. Glaser, Y .-C. Hsu, and Z. Kira, “Posterior re- calibration for imbalanced datasets,” inAdvances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, Eds., vol. 33, 2020, pp. 8101–8113

2020
[181]

A unified generalization analysis of re-weighting and logit-adjustment for imbalanced learning,

Z. Wang, Q. Xu, Z. Yang, Y . He, X. Cao, and Q. Huang, “A unified generalization analysis of re-weighting and logit-adjustment for imbalanced learning,”Advances in Neural Information Processing Systems, vol. 36, 2024

2024
[182]

Bagging predictors,

L. Breiman, “Bagging predictors,”Machine learning, vol. 24, pp. 123– 140, 1996

1996
[183]

Diversity analysis on imbalanced data sets by using ensemble models,

S. Wang and X. Yao, “Diversity analysis on imbalanced data sets by using ensemble models,” in2009 IEEE symposium on computational intelligence and data mining. IEEE, 2009, pp. 324–331

2009
[184]

Roughly balanced bagging for imbalanced data,

S. Hido, H. Kashima, and Y . Takahashi, “Roughly balanced bagging for imbalanced data,”Statistical Analysis and Data Mining: The ASA Data Science Journal, vol. 2, no. 5-6, pp. 412–426, 2009

2009
[185]

Neighbourhood sampling in bagging for imbalanced data,

J. Błaszczy ´nski and J. Stefanowski, “Neighbourhood sampling in bagging for imbalanced data,”Neurocomputing, vol. 150, pp. 529–542, 2015. 32

2015
[186]

Multi-class adaboost,

T. Hastie, S. Rosset, J. Zhu, and H. Zou, “Multi-class adaboost,” Statistics and its Interface, vol. 2, no. 3, pp. 349–360, 2009

2009
[187]

Greedy function approximation: a gradient boosting machine,

J. H. Friedman, “Greedy function approximation: a gradient boosting machine,”Annals of statistics, pp. 1189–1232, 2001

2001
[188]

Xgboost: A scalable tree boosting system,

T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” inProceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 2016, pp. 785–794

2016
[189]

Smote- boost: Improving prediction of the minority class in boosting,

N. V . Chawla, A. Lazarevic, L. O. Hall, and K. W. Bowyer, “Smote- boost: Improving prediction of the minority class in boosting,” in European conference on principles of data mining and knowledge discovery. Springer, 2003, pp. 107–119

2003
[190]

Msmote: Improving classifica- tion performance when training data is imbalanced,

S. Hu, Y . Liang, L. Ma, and Y . He, “Msmote: Improving classifica- tion performance when training data is imbalanced,” in2009 second international workshop on computer science and engineering, vol. 2. IEEE, 2009, pp. 13–17

2009
[191]

Ramoboost: Ranked minority oversampling in boosting,

S. Chen, H. He, and E. A. Garcia, “Ramoboost: Ranked minority oversampling in boosting,”IEEE Transactions on Neural Networks, vol. 21, no. 10, pp. 1624–1642, 2010

2010
[192]

Rusboost: A hybrid approach to alleviating class imbalance,

C. Seiffert, T. M. Khoshgoftaar, J. Van Hulse, and A. Napolitano, “Rusboost: A hybrid approach to alleviating class imbalance,”IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, vol. 40, no. 1, pp. 185–197, 2009

2009
[193]

Eusboost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling,

M. Galar, A. Fern ´andez, E. Barrenechea, and F. Herrera, “Eusboost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling,”Pattern recognition, vol. 46, no. 12, pp. 3460–3471, 2013

2013
[194]

Rhsboost: Improving classification performance in imbalance data,

J. Gong and H. Kim, “Rhsboost: Improving classification performance in imbalance data,”Computational Statistics & Data Analysis, vol. 111, pp. 1–13, 2017

2017
[195]

Rose: a package for binary imbalanced learning

N. Lunardon, G. Menardi, and N. Torelli, “Rose: a package for binary imbalanced learning.”R journal, vol. 6, no. 1, 2014

2014
[196]

Liuboost: locality informed under-boosting for imbalanced data classification,

S. Ahmed, F. Rayhan, A. Mahbub, M. Rafsan Jani, S. Shatabda, and D. M. Farid, “Liuboost: locality informed under-boosting for imbalanced data classification,” inEmerging Technologies in Data Mining and Information Security: Proceedings of IEMIS 2018, Volume

2018
[197]

Springer, 2019, pp. 133–144

2019
[198]

A hybrid under-sampling method (husboost) to classify imbalanced data,

M. H. Popel, K. M. Hasib, S. A. Habib, and F. M. Shah, “A hybrid under-sampling method (husboost) to classify imbalanced data,” in2018 21st international conference of computer and information technology (ICCIT). IEEE, 2018, pp. 1–7

2018
[199]

Cost-sensitive learning and the class imbalance problem,

C. X. Ling and V . S. Sheng, “Cost-sensitive learning and the class imbalance problem,”Encyclopedia of machine learning, vol. 2011, pp. 231–235, 2008

2011
[200]

Adacost: misclassifi- cation cost-sensitive boosting,

W. Fan, S. J. Stolfo, J. Zhang, and P. K. Chan, “Adacost: misclassifi- cation cost-sensitive boosting,” inIcml, vol. 99, 1999, pp. 97–105

1999
[201]

A comparative study of cost-sensitive boosting algo- rithms,

K. M. Ting, “A comparative study of cost-sensitive boosting algo- rithms,” inProc. of the 17th International Conference on Machine Learning (ICML), 2000, 2000

2000
[202]

Cost-sensitive boosting for classification of imbalanced data,

Y . Sun, M. S. Kamel, A. K. Wong, and Y . Wang, “Cost-sensitive boosting for classification of imbalanced data,”Pattern recognition, vol. 40, no. 12, pp. 3358–3378, 2007

2007
[203]

Knowledge distillation: A survey,

J. Gou, B. Yu, S. J. Maybank, and D. Tao, “Knowledge distillation: A survey,”International Journal of Computer Vision, vol. 129, pp. 1789– 1819, 2021

2021
[204]

Ensemble knowledge distilla- tion for learning improved and efficient networks,

U. Asif, J. Tang, and S. Harrer, “Ensemble knowledge distilla- tion for learning improved and efficient networks,”arXiv preprint arXiv:1909.08097, 2019

1909 arXiv
[205]

Distilling knowledge from ensembles of neural networks for speech recognition

Y . Chebotar and A. Waters, “Distilling knowledge from ensembles of neural networks for speech recognition.” inInterspeech, 2016, pp. 3439–3443

2016
[206]

Efficient knowledge distillation from an ensemble of teachers

T. Fukuda, M. Suzuki, G. Kurata, S. Thomas, J. Cui, and B. Ramabhad- ran, “Efficient knowledge distillation from an ensemble of teachers.” inInterspeech, 2017, pp. 3697–3701

2017
[207]

Adaptive multi-teacher multi-level knowledge distillation,

Y . Liu, W. Zhang, and J. Wang, “Adaptive multi-teacher multi-level knowledge distillation,”Neurocomputing, vol. 415, pp. 106–113, 2020

2020
[208]

Reinforced multi-teacher selection for knowledge distillation,

F. Yuan, L. Shou, J. Pei, W. Lin, M. Gong, Y . Fu, and D. Jiang, “Reinforced multi-teacher selection for knowledge distillation,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 16, 2021, pp. 14 284–14 291

2021
[209]

Highlight every step: Knowledge distillation via collaborative teaching,

H. Zhao, X. Sun, J. Dong, C. Chen, and Z. Dong, “Highlight every step: Knowledge distillation via collaborative teaching,”IEEE Transactions on Cybernetics, vol. 52, no. 4, pp. 2070–2081, 2020

2020
[210]

Model compression with two-stage multi-teacher knowledge distillation for web question answering system,

Z. Yang, L. Shou, M. Gong, W. Lin, and D. Jiang, “Model compression with two-stage multi-teacher knowledge distillation for web question answering system,” inProceedings of the 13th International Confer- ence on Web Search and Data Mining, 2020, pp. 690–698

2020
[211]

Collaborative global- local structure network with knowledge distillation for imbalanced data classification,

F. Wu, Z. Liu, Z. Zhang, J. Liu, and L. Wang, “Collaborative global- local structure network with knowledge distillation for imbalanced data classification,”IEEE Transactions on Circuits and Systems for Video Technology, 2024

2024
[212]

Collaborative multi-teacher knowl- edge distillation for learning low bit-width deep neural networks,

C. Pham, T. Hoang, and T.-T. Do, “Collaborative multi-teacher knowl- edge distillation for learning low bit-width deep neural networks,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023, pp. 6435–6443

2023
[213]

Solv- ing long-tailed recognition with deep realistic taxonomic classifier,

T.-Y . Wu, P. Morgado, P. Wang, C.-H. Ho, and N. Vasconcelos, “Solv- ing long-tailed recognition with deep realistic taxonomic classifier,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part VIII 16. Springer, 2020, pp. 171–189

2020
[214]

Adversarial robustness under long-tailed distribution,

T. Wu, Z. Liu, Q. Huang, Y . Wang, and D. Lin, “Adversarial robustness under long-tailed distribution,” inProceedings of the IEEE/CVF confer- ence on computer vision and pattern recognition, 2021, pp. 8659–8668

2021
[215]

Global and local mixture consistency cumulative learning for long-tailed visual recognitions,

F. Du, P. Yang, Q. Jia, F. Nan, X. Chen, and Y . Yang, “Global and local mixture consistency cumulative learning for long-tailed visual recognitions,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 15 814–15 823

2023
[216]

A simple long-tailed recognition baseline via vision-language model,

T. Ma, S. Geng, M. Wang, J. Shao, J. Lu, H. Li, P. Gao, and Y . Qiao, “A simple long-tailed recognition baseline via vision-language model,” arXiv preprint arXiv:2111.14745, 2021

2021 arXiv
[217]

Parameter-efficient long-tailed recognition,

J.-X. Shi, T. Wei, Z. Zhou, X.-Y . Han, J.-J. Shao, and Y .- F. Li, “Parameter-efficient long-tailed recognition,”arXiv preprint arXiv:2309.10019, 2023

2023 arXiv
[218]

Imbalance problems in object detection: A review,

K. Oksuz, B. C. Cam, S. Kalkan, and E. Akbas, “Imbalance problems in object detection: A review,”IEEE transactions on pattern analysis and machine intelligence, vol. 43, no. 10, pp. 3388–3415, 2020

2020
[219]

Rich feature hierarchies for accurate object detection and semantic segmentation,

R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” inProceedings of the IEEE conference on computer vision and pattern recognition, 2014, pp. 580–587

2014
[220]

Ssd: Single shot multibox detector,

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y . Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” inComputer Vision– ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14. Springer, 2016, pp. 21– 37

2016
[221]

Focal loss for dense object detection,

T.-Y . Ross and G. Doll ´ar, “Focal loss for dense object detection,” in proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 2980–2988

2017
[222]

Gradient harmonized single-stage detec- tor,

B. Li, Y . Liu, and X. Wang, “Gradient harmonized single-stage detec- tor,” inProceedings of the AAAI conference on artificial intelligence, vol. 33, no. 01, 2019, pp. 8577–8584

2019
[223]

Prime sample attention in object detection,

Y . Cao, K. Chen, C. C. Loy, and D. Lin, “Prime sample attention in object detection,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 11 583–11 591

2020
[224]

Residual objectness for imbalance reduction,

J. Chen, D. Liu, B. Luo, X. Peng, T. Xu, and E. Chen, “Residual objectness for imbalance reduction,”Pattern Recognition, vol. 130, p. 108781, 2022

2022
[225]

Towards accurate one-stage object detection with ap-loss,

K. Chen, J. Li, W. Lin, J. See, J. Wang, L. Duan, Z. Chen, C. He, and J. Zou, “Towards accurate one-stage object detection with ap-loss,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 5119–5127

2019
[226]

A-fast-rcnn: Hard positive generation via adversary for object detection,

X. Wang, A. Shrivastava, and A. Gupta, “A-fast-rcnn: Hard positive generation via adversary for object detection,” inProceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 2606–2615

2017
[227]

Learning to generate synthetic data via compositing,

S. Tripathi, S. Chandra, A. Agrawal, A. Tyagi, J. M. Rehg, and V . Chari, “Learning to generate synthetic data via compositing,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 461–470

2019
[228]

Feature pyramid networks for object detection,

T.-Y . Lin, P. Doll´ar, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” inProceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 2117–2125

2017
[229]

Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations,

C. H. Sudre, W. Li, T. Vercauteren, S. Ourselin, and M. Jorge Car- doso, “Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations,” inDeep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: Third Inte...

2017
[230]

Generalized overlap mea- sures for evaluation and validation in medical image analysis,

W. R. Crum, O. Camara, and D. L. Hill, “Generalized overlap mea- sures for evaluation and validation in medical image analysis,”IEEE transactions on medical imaging, vol. 25, no. 11, pp. 1451–1461, 2006. 33

2006
[231]

Loss max-pooling for semantic image segmentation,

S. Rota Bulo, G. Neuhold, and P. Kontschieder, “Loss max-pooling for semantic image segmentation,” inProceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 2126–2135

2017
[232]

Combo loss: Handling input and output imbalance in multi-organ segmentation,

S. A. Taghanaki, Y . Zheng, S. K. Zhou, B. Georgescu, P. Sharma, D. Xu, D. Comaniciu, and G. Hamarneh, “Combo loss: Handling input and output imbalance in multi-organ segmentation,”Computerized Medical Imaging and Graphics, vol. 75, pp. 24–33, 2019

2019
[233]

Under- standing imbalanced semantic segmentation through neural collapse,

Z. Zhong, J. Cui, Y . Yang, X. Wu, X. Qi, X. Zhang, and J. Jia, “Under- standing imbalanced semantic segmentation through neural collapse,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2023, pp. 19 550–19 560

2023
[234]

A survey of methods for addressing class imbalance in deep-learning based natural language processing,

S. Henning, W. Beluch, A. Fraser, and A. Friedrich, “A survey of methods for addressing class imbalance in deep-learning based natural language processing,”arXiv preprint arXiv:2210.04675, 2022

2022 arXiv
[235]

Retrieval-augmented generation for ai-generated content: A survey,

P. Zhao, H. Zhang, Q. Yu, Z. Wang, Y . Geng, F. Fu, L. Yang, W. Zhang, and B. Cui, “Retrieval-augmented generation for ai-generated content: A survey,”arXiv preprint arXiv:2402.19473, 2024

2024 arXiv
[236]

Qaencoder: Towards aligned representation learning in question answering system,

Z. Wang, Q. Yu, S. Wei, Z. Li, F. Xiong, X. Wang, S. Niu, H. Liang, and W. Zhang, “Qaencoder: Towards aligned representation learning in question answering system,”arXiv preprint arXiv:2409.20434, 2024

2024 arXiv
[237]

Imbalanced twitter sentiment analysis using minority oversampling,

K. Ghosh, A. Banerjee, S. Chatterjee, and S. Sen, “Imbalanced twitter sentiment analysis using minority oversampling,” in2019 IEEE 10th international conference on awareness science and technology (iCAST). IEEE, 2019, pp. 1–5

2019
[238]

An evaluation of svm and naive bayes with smote on sentiment analysis data set,

A. C. Flores, R. I. Icoy, C. F. Pe ˜na, and K. D. Gorro, “An evaluation of svm and naive bayes with smote on sentiment analysis data set,” in 2018 International Conference on Engineering, Applied Sciences, and Technology (ICEAST). IEEE, 2018, pp. 1–4

2018
[239]

Sen- timent analysis on e-sports for education curriculum using naive bayes and support vector machine,

R. Ardianto, T. Rivanie, Y . Alkhalifi, F. S. Nugraha, and W. Gata, “Sen- timent analysis on e-sports for education curriculum using naive bayes and support vector machine,”Jurnal Ilmu Komputer dan Informasi, vol. 13, no. 2, pp. 109–122, 2020

2020
[240]

Using word embedding and ensemble learning for highly imbalanced data sentiment analysis in short arabic text,

S. Al-Azani and E.-S. M. El-Alfy, “Using word embedding and ensemble learning for highly imbalanced data sentiment analysis in short arabic text,”Procedia Computer Science, vol. 109, pp. 359–366, 2017

2017
[241]

How translation alters sentiment,

S. M. Mohammad, M. Salameh, and S. Kiritchenko, “How translation alters sentiment,”Journal of Artificial Intelligence Research, vol. 55, pp. 95–130, 2016

2016
[242]

Sentiment analysis on evolving social streams: How self-report imbalances can help,

P. C. Guerra, W. Meira Jr, and C. Cardie, “Sentiment analysis on evolving social streams: How self-report imbalances can help,” in Proceedings of the 7th ACM international conference on Web search and data mining, 2014, pp. 443–452

2014
[243]

Sentiment analysis of customers’ reviews using a hybrid evolutionary svm-based approach in an imbalanced data distribution,

R. Obiedat, R. Qaddoura, A.-Z. Ala’M, L. Al-Qaisi, O. Harfoushi, M. Alrefai, and H. Faris, “Sentiment analysis of customers’ reviews using a hybrid evolutionary svm-based approach in an imbalanced data distribution,”IEEE Access, vol. 10, pp. 22 260–22 273, 2022

2022
[244]

A comparative analysis of machine learning models for banking news extraction by multiclass classification with imbalanced datasets of financial news: challenges and solutions,

V . Dogra, S. Verma, K. Verma, N. Jhanjhi, U. Ghosh, and D.-N. Le, “A comparative analysis of machine learning models for banking news extraction by multiclass classification with imbalanced datasets of financial news: challenges and solutions,”International Journal of Interac...

2022
[245]

News classification for identifying traffic incident points in a spanish- speaking country: A real-world case study of class imbalance learning,

G. Rivera, R. Florencia, V . Garc ´ıa, A. Ruiz, and J. P. S ´anchez-Sol´ıs, “News classification for identifying traffic incident points in a spanish- speaking country: A real-world case study of class imbalance learning,” Applied Sciences, vol. 10, no. 18, p. 6253, 2020

2020
[246]

Banfak- enews: A dataset for detecting fake news in bangla,

M. Z. Hossain, M. A. Rahman, M. S. Islam, and S. Kar, “Banfak- enews: A dataset for detecting fake news in bangla,”arXiv preprint arXiv:2004.08789, 2020

2004 arXiv
[247]

Augfake-bert: handling imbalance through augmentation of fake news using bert to enhance the performance of fake news classification,

A. J. Keya, M. A. H. Wadud, M. Mridha, M. Alatiyyah, and M. A. Hamid, “Augfake-bert: handling imbalance through augmentation of fake news using bert to enhance the performance of fake news classification,”Applied Sciences, vol. 12, no. 17, p. 8398, 2022

2022
[248]

Strategies for enhancing the performance of news article classification in bangla: Handling imbalance and interpretation,

K. M. Hasib, N. A. Towhid, K. O. Faruk, J. Al Mahmud, and M. Mridha, “Strategies for enhancing the performance of news article classification in bangla: Handling imbalance and interpretation,”En- gineering Applications of Artificial Intelligence, vol. 125, p. 106688, 2023

2023
[249]

Bangla newspaper dataset,

Z. A. Nazi, “Bangla newspaper dataset,” 2020. [Online]. Available: https://www.kaggle.com/dsv/1576225

2020
[250]

Exploring the limits of transfer learning with a unified text- to-text transformer,

C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, and P. J. Liu, “Exploring the limits of transfer learning with a unified text- to-text transformer,”Journal of Machine Learning Research, vol. 21, no. 140, pp. 1–67, 2020

2020
[251]

From quantity to quality: Boosting llm performance with self-guided data selection for instruction tuning,

M. Li, Y . Zhang, Z. Li, J. Chen, L. Chen, N. Cheng, and J. Xiao, “From quantity to quality: Boosting llm performance with self-guided data selection for instruction tuning,”arXiv preprint arXiv:2308.12032, 2023

2023 arXiv
[252]

Numinamath: The largest public dataset in ai4maths with 860k pairs of competition math problems and solutions,

J. Li, E. Beeching, L. Tunstall, B. Lipkin, R. Soletskyi, S. Huang, and S. Polu, “Numinamath: The largest public dataset in ai4maths with 860k pairs of competition math problems and solutions,” 2024, hugging Face repository

2024
[253]

Learning from mistakes makes llm better reasoner,

S. An, Z. Ma, Z. Lin, N. Zheng, J. G. Lou, and W. Chen, “Learning from mistakes makes llm better reasoner,”arXiv preprint arXiv:2310.20689, 2023

2023 arXiv
[254]

Distributed graph neural network training: A survey,

Y . Shao, H. Li, X. Gu, H. Yin, Y . Li, X. Miao, W. Zhang, B. Cui, and L. Chen, “Distributed graph neural network training: A survey,”ACM Computing Surveys, vol. 56, no. 8, pp. 1–39, 2024

2024
[255]

Accelerating scalable graph neural network inference with node-adaptive propagation,

X. Gao, W. Zhang, J. Yu, Y . Shao, Q. V . H. Nguyen, B. Cui, and H. Yin, “Accelerating scalable graph neural network inference with node-adaptive propagation,” inIEEE 40th International Conference on Data Engineering, 2024, pp. 3042–3055

2024
[256]

Contrastive graph condensation: Advancing data versatility through self-supervised learning,

X. Gao, Y . Li, T. Chen, G. Ye, W. Zhang, and H. Yin, “Contrastive graph condensation: Advancing data versatility through self-supervised learning,”arXiv preprint arXiv:2411.17063, 2024

2024
[257]

Rethinking and accelerating graph condensation: A training- free approach with class partition,

X. Gao, T. Chen, W. Zhang, J. Yu, G. Ye, Q. V . H. Nguyen, and H. Yin, “Rethinking and accelerating graph condensation: A training- free approach with class partition,” inProceedings of the ACM Web Conference, 2025

2025
[258]

Robgc: Towards robust graph condensation,

X. Gao, H. Yin, T. Chen, G. Ye, W. Zhang, and B. Cui, “Robgc: Towards robust graph condensation,”arXiv, 2024

2024
[259]

Semantic-aware node synthesis for imbalanced heterogeneous infor- mation networks,

X. Gao, W. Zhang, T. Chen, J. Yu, H. Q. V . Nguyen, and H. Yin, “Semantic-aware node synthesis for imbalanced heterogeneous infor- mation networks,” inProceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023, pp. 545–555

2023
[260]

Graph condensation for open-world graph learning,

X. Gao, T. Chen, W. Zhang, Y . Li, X. Sun, and H. Yin, “Graph condensation for open-world graph learning,” inProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024, pp. 851–862

2024
[261]

Graph condensation: A survey,

X. Gao, J. Yu, T. Chen, G. Ye, W. Zhang, and H. Yin, “Graph condensation: A survey,”IEEE Transactions on Knowledge and Data Engineering, 2025

2025
[262]

Graphmixup: Im- proving class-imbalanced node classification by reinforcement mixup and self-supervised context prediction,

L. Wu, J. Xia, Z. Gao, H. Lin, C. Tan, and S. Z. Li, “Graphmixup: Im- proving class-imbalanced node classification by reinforcement mixup and self-supervised context prediction,” inJoint European conference on machine learning and knowledge discovery in databases. Springer, 20...

2022
[263]

Graphens: Neighbor-aware ego network synthesis for class-imbalanced node classification,

J. Park, J. Song, and E. Yang, “Graphens: Neighbor-aware ego network synthesis for class-imbalanced node classification,” inInternational conference on learning representations, 2021

2021
[264]

Graphsha: Syn- thesizing harder samples for class-imbalanced node classification,

W.-Z. Li, C.-D. Wang, H. Xiong, and J.-H. Lai, “Graphsha: Syn- thesizing harder samples for class-imbalanced node classification,” inProceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining, 2023, pp. 1328–1340

2023
[265]

Imgagn: Imbalanced network embedding via generative adversarial graph networks,

L. Qu, H. Zhu, R. Zheng, Y . Shi, and H. Yin, “Imgagn: Imbalanced network embedding via generative adversarial graph networks,” in Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, 2021, pp. 1390–1398

2021
[266]

Graphsr: A data augmentation algorithm for imbalanced node classification,

M. Zhou and Z. Gong, “Graphsr: A data augmentation algorithm for imbalanced node classification,” inProceedings of the AAAI Confer- ence on artificial intelligence, vol. 37, no. 4, 2023, pp. 4954–4962

2023
[267]

Bim: improving graph neural networks with balanced influence maximization,

W. Zhang, X. Gao, L. Yang, M. Cao, P. Huang, J. Shan, H. Yin, and B. Cui, “Bim: improving graph neural networks with balanced influence maximization,” inIEEE 40th International Conference on Data Engineering (ICDE), 2024, pp. 2931–2944

2024
[268]

Lte4g: Long-tail experts for graph neural networks,

S. Yun, K. Kim, K. Yoon, and C. Park, “Lte4g: Long-tail experts for graph neural networks,” inProceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022, pp. 2434–2443

2022
[269]

Tail-gnn: Tail-node graph neural networks,

Z. Liu, T.-K. Nguyen, and Y . Fang, “Tail-gnn: Tail-node graph neural networks,” inProceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, 2021, pp. 1109–1119

2021
[270]

Topology-imbalance learning for semi-supervised node classification,

D. Chen, Y . Lin, G. Zhao, X. Ren, P. Li, J. Zhou, and X. Sun, “Topology-imbalance learning for semi-supervised node classification,” Advances in Neural Information Processing Systems, vol. 34, pp. 29 885–29 897, 2021

2021
[271]

Position-aware structure learning for graph topology-imbalance by relieving under-reaching and over-squashing,

Q. Sun, J. Li, H. Yuan, X. Fu, H. Peng, C. Ji, Q. Li, and P. S. Yu, “Position-aware structure learning for graph topology-imbalance by relieving under-reaching and over-squashing,” inProceedings of the 31st ACM International Conference on Information & Knowledge Management, 20...

2022
[272]

Few-shot link prediction for event-based social networks via meta-learning,

X. Zhu, P. Luo, Z. Zhao, T. Xu, A. Lizhiyu, Y . Yu, X. Li, and E. Chen, “Few-shot link prediction for event-based social networks via meta-learning,” inInternational Conference on Database Systems for Advanced Applications. Springer, 2023, pp. 31–41

2023
[273]

Deep graph learning for anomalous citation detection,

J. Liu, F. Xia, X. Feng, J. Ren, and H. Liu, “Deep graph learning for anomalous citation detection,”IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 6, pp. 2543–2557, 2022

2022
[274]

Live-streaming fraud detection: A heterogeneous graph neural network approach,

Z. Li, H. Wang, P. Zhang, P. Hui, J. Huang, J. Liao, J. Zhang, and J. Bu, “Live-streaming fraud detection: A heterogeneous graph neural network approach,” inProceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021, pp. 3670– 3678

2021
[275]

Imbalanced graph classi- fication via graph-of-graph neural networks,

Y . Wang, Y . Zhao, N. Shah, and T. Derr, “Imbalanced graph classi- fication via graph-of-graph neural networks,” inProceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022, pp. 2067–2076

2022
[276]

Semi-supervised graph imbalanced regression,

G. Liu, T. Zhao, E. Inae, T. Luo, and M. Jiang, “Semi-supervised graph imbalanced regression,” inProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023, pp. 1453–1465

2023
[277]

An empirical study of learning from imbalanced data using random forest,

T. M. Khoshgoftaar, M. Golawala, and J. Van Hulse, “An empirical study of learning from imbalanced data using random forest,” in 19th IEEE international conference on tools with artificial intelligence (ICTAI 2007), vol. 2. IEEE, 2007, pp. 310–317

2007
[278]

Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning,

G. Lema ˆıtre, F. Nogueira, and C. K. Aridas, “Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning,”Journal of Machine Learning Research, vol. 18, no. 17, pp. 1–5, 2017. [Online]. Available: http://jmlr.org/papers/v18/16-365

2017
[279]

Open-source imbalance-overlap,

M. Santos, “Open-source imbalance-overlap,” https://github.com/miriamspsantos/open-source-imbalance-overlap
[280]

Igl-bench: Establishing the comprehensive benchmark for imbalanced graph learning,

J. Qin, H. Yuan, Q. Sun, L. Xu, J. Yuan, P. Huang, and P. S. Yu, “Igl-bench: Establishing the comprehensive benchmark for imbalanced graph learning,”arXiv preprint arXiv:2406.09870, 2024

2024 arXiv
[281]

Imbens: Ensemble class- imbalanced learning in python,

Z. Liu, J. Kang, H. Tong, and Y . Chang, “Imbens: Ensemble class- imbalanced learning in python,”arXiv preprint arXiv:2111.12776, 2021

2021 arXiv
[282]

balance–a python package for bal- ancing biased data samples,

T. Sarig, T. Galili, and R. Eilat, “balance–a python package for bal- ancing biased data samples,”arXiv preprint arXiv:2307.06024, 2023

2023 arXiv
[283]

Semi-supervised imbalanced multi-label classification with label propagation,

G. Du, J. Zhang, N. Zhang, H. Wu, P. Wu, and S. Li, “Semi-supervised imbalanced multi-label classification with label propagation,”Pattern Recognition, vol. 150, p. 110358, 2024

2024
[284]

Ranksim: Ranking similar- ity regularization for deep imbalanced regression,

Y . Gong, G. Mori, and F. Tung, “Ranksim: Ranking similar- ity regularization for deep imbalanced regression,”arXiv preprint arXiv:2205.15236, 2022

2022 arXiv
[285]

Delving into deep imbalanced regression,

Y . Yang, K. Zha, Y . Chen, H. Wang, and D. Katabi, “Delving into deep imbalanced regression,” inInternational conference on machine learning. PMLR, 2021, pp. 11 842–11 851

2021
[286]

Co- modality graph contrastive learning for imbalanced node classification,

Y . Qian, C. Zhang, Y . Zhang, Q. Wen, Y . Ye, and C. Zhang, “Co- modality graph contrastive learning for imbalanced node classification,” Advances in Neural Information Processing Systems, vol. 35, pp. 15 862–15 874, 2022

2022
[287]

Epic: Effective prompting for imbalanced-class data synthesis in tabular data classification via large language models,

J. Kim, T. Kim, and J. Choo, “Epic: Effective prompting for imbalanced-class data synthesis in tabular data classification via large language models,”Advances in Neural Information Processing Systems, vol. 37, pp. 31 504–31 542, 2024

2024
[288]

Language- interfaced tabular oversampling via progressive imputation and self- authentication,

J. Y . Yang, G. Park, J. Kim, H. Jang, and E. Yang, “Language- interfaced tabular oversampling via progressive imputation and self- authentication,” inThe Twelfth International Conference on Learning Representations, 2024

2024
[289]

Improving text classification with large language model-based data augmentation,

H. Zhao, H. Chen, T. A. Ruggles, Y . Feng, D. Singh, and H.-J. Yoon, “Improving text classification with large language model-based data augmentation,”Electronics, vol. 13, no. 13, p. 2535, 2024

2024
[290]

Meta learning with language mod- els: Challenges and opportunities in the classification of imbalanced text,

A. Vassilev, H. Jin, and M. Hasan, “Meta learning with language mod- els: Challenges and opportunities in the classification of imbalanced text,”arXiv preprint arXiv:2310.15019, 2023. Xinyi Gaois a Postdoctoral Researcher at the School of Electrical Engineering and Computer Sc...

2023 arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

Learning from imbalanced data,

H. He and E. A. Garcia, “Learning from imbalanced data,”IEEE Transactions on knowledge and data engineering, vol. 21, no. 9, pp. 1263–1284, 2009

2009

[2] [2]

Learning from imbalanced data: open challenges and future directions,

B. Krawczyk, “Learning from imbalanced data: open challenges and future directions,”Progress in artificial intelligence, vol. 5, no. 4, pp. 221–232, 2016

2016

[3] [3]

Learning from class-imbalanced data: Review of methods and applications,

G. Haixiang, L. Yijing, J. Shang, G. Mingyun, H. Yuanyue, and G. Bing, “Learning from class-imbalanced data: Review of methods and applications,”Expert systems with applications, vol. 73, pp. 220– 239, 2017

2017

[4] [4]

Deep long-tailed learning: A survey,

Y . Zhang, B. Kang, B. Hooi, S. Yan, and J. Feng, “Deep long-tailed learning: A survey,”IEEE transactions on pattern analysis and machine intelligence, vol. 45, no. 9, pp. 10 795–10 816, 2023

2023

[5] [5]

Smote: synthetic minority over-sampling technique,

N. V . Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “Smote: synthetic minority over-sampling technique,”Journal of ar- tificial intelligence research, vol. 16, pp. 321–357, 2002

2002

[6] [6]

mixup: Beyond empirical risk minimization,

H. Zhang, M. Cisse, Y . N. Dauphin, and D. Lopez-Paz, “mixup: Beyond empirical risk minimization,”arXiv preprint arXiv:1710.09412, 2017

arXiv 2017

[7] [7]

A distance-based over-sampling method for learning from imbalanced data sets

J. De La Calleja and O. Fuentes, “A distance-based over-sampling method for learning from imbalanced data sets.” inFLAIRS conference, 2007, pp. 634–635

2007

[8] [8]

On the use of surround- ing neighbors for synthetic over-sampling of the minority class,

V . Garc ´ıa, J. S ´anchez, and R. Mollineda, “On the use of surround- ing neighbors for synthetic over-sampling of the minority class,” inProceedings of the 8th conference on Simulation, modelling and optimization, 2008, pp. 389–394

2008