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 · ORCID

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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Pith tools

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

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