A Comprehensive Survey on Imbalanced Data Learning | Paper Citation Record

Observation edcade7a-949f-4c7f-b822-c4014c5f32ad · outbound

This paper cites Learning from imbalanced data,.

A Comprehensive Survey on Imbalanced Data Learning Learning from imbalanced data,

Reference 1

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This paper cites Learning from imbalanced data: open challenges and future directions,.

A Comprehensive Survey on Imbalanced Data Learning Learning from imbalanced data: open challenges and future directions,

Reference 2

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Observation 2908cdbf-17f0-40a6-bcf7-a7cae930f730 · outbound

This paper cites Learning from class-imbalanced data: Review of methods and applications,.

A Comprehensive Survey on Imbalanced Data Learning Learning from class-imbalanced data: Review of methods and applications,

Reference 3

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This paper cites Deep long-tailed learning: A survey,.

A Comprehensive Survey on Imbalanced Data Learning Deep long-tailed learning: A survey,

Reference 4

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This paper cites Smote: synthetic minority over-sampling technique,.

A Comprehensive Survey on Imbalanced Data Learning Smote: synthetic minority over-sampling technique,

Reference 5

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This paper cites mixup: Beyond Empirical Risk Minimization.

A Comprehensive Survey on Imbalanced Data Learning mixup: Beyond Empirical Risk Minimization

Reference 6

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Observation d85e9f87-1eaa-4523-a38c-5152d89ebf4e · outbound

This paper cites A distance-based over-sampling method for learning from imbalanced data sets.

A Comprehensive Survey on Imbalanced Data Learning A distance-based over-sampling method for learning from imbalanced data sets

Reference 7

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Observation 6cb819fa-a0ec-4c73-9467-f1f48b144dfd · outbound

This paper cites On the use of surround- ing neighbors for synthetic over-sampling of the minority class,.

A Comprehensive Survey on Imbalanced Data Learning On the use of surround- ing neighbors for synthetic over-sampling of the minority class,

Reference 8

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This paper cites Borderline-smote: a new over- sampling method in imbalanced data sets learning,.

A Comprehensive Survey on Imbalanced Data Learning Borderline-smote: a new over- sampling method in imbalanced data sets learning,

Reference 9

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Observation f9728e36-eb4f-48e3-95e8-35c86adb68dd · outbound

This paper cites Adasyn: Adaptive synthetic sampling approach for imbalanced learning,.

A Comprehensive Survey on Imbalanced Data Learning Adasyn: Adaptive synthetic sampling approach for imbalanced learning,

Reference 10

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Observation c5fa1f00-46a9-41de-b526-cde592932cf8 · outbound

This paper cites Msmote: Improving classifica- tion performance when training data is imbalanced,.

A Comprehensive Survey on Imbalanced Data Learning Msmote: Improving classifica- tion performance when training data is imbalanced,

Reference 11

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Observation df3e66aa-2e3c-4d24-a367-54185736aee5 · outbound

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

A Comprehensive Survey on Imbalanced Data Learning An empirical comparison and evaluation of minority oversampling techniques on a large number of imbalanced datasets,

Reference 12

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Observation dba691dc-6fc4-4027-8e1b-b0476e4df469 · outbound

This paper cites Remix: rebalanced mixup,.

A Comprehensive Survey on Imbalanced Data Learning Remix: rebalanced mixup,

Reference 13

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Observation d470a9fa-d81e-4d6a-9a66-0a381b248391 · outbound

This paper cites Mixboost: Synthetic oversampling using boosted mixup for handling extreme imbalance,.

A Comprehensive Survey on Imbalanced Data Learning Mixboost: Synthetic oversampling using boosted mixup for handling extreme imbalance,

Reference 14

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Observation 7cce30d7-ce06-463d-986c-c97de33a079a · outbound

This paper cites Balanced- mixup for highly imbalanced medical image classification,.

A Comprehensive Survey on Imbalanced Data Learning Balanced- mixup for highly imbalanced medical image classification,

Reference 15

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Observation 182189c2-13e6-4189-85c3-af351f2530e5 · outbound

This paper cites Label-occurrence-balanced mixup for long-tailed recognition,.

A Comprehensive Survey on Imbalanced Data Learning Label-occurrence-balanced mixup for long-tailed recognition,

Reference 16

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Observation 2c40b9fb-3897-42c4-8fc6-2f7ed8a8b5ca · outbound

This paper cites Kernel-based smote for svm classification of imbalanced datasets,.

A Comprehensive Survey on Imbalanced Data Learning Kernel-based smote for svm classification of imbalanced datasets,

Reference 17

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This paper cites Kerneladasyn: Kernel based adaptive synthetic data generation for imbalanced learning,.

A Comprehensive Survey on Imbalanced Data Learning Kerneladasyn: Kernel based adaptive synthetic data generation for imbalanced learning,

Reference 18

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Observation 8ab8a43a-bed0-4e9c-a045-a8d381637bd4 · outbound

This paper cites Variational autoencoder based synthetic data generation for imbalanced learning,.

A Comprehensive Survey on Imbalanced Data Learning Variational autoencoder based synthetic data generation for imbalanced learning,

Reference 19

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Observation 10b1db76-1a42-4074-8291-03eeb5d54be3 · outbound

This paper cites Generative adversarial net- works,.

A Comprehensive Survey on Imbalanced Data Learning Generative adversarial net- works,

Reference 20

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This paper cites Effective data generation for imbalanced learning using conditional generative adversarial networks,.

A Comprehensive Survey on Imbalanced Data Learning Effective data generation for imbalanced learning using conditional generative adversarial networks,

Reference 21

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This paper cites Conditional Generative Adversarial Nets.

A Comprehensive Survey on Imbalanced Data Learning Conditional Generative Adversarial Nets

Reference 22

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This paper cites BAGAN: Data Augmentation with Balancing GAN.

A Comprehensive Survey on Imbalanced Data Learning BAGAN: Data Augmentation with Balancing GAN

Reference 23

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Observation 6ff378cb-0c1f-416a-a3e0-f66b9bbdbde5 · outbound

This paper cites Supervised class distribution learning for gans-based imbalanced classification,.

A Comprehensive Survey on Imbalanced Data Learning Supervised class distribution learning for gans-based imbalanced classification,

Reference 24

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

A Comprehensive Survey on Imbalanced Data Learning Rvgan-tl: A generative adversarial networks and transfer learning- based hybrid approach for imbalanced data classification,

Reference 25

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Observation 69af5667-1383-42e3-b31e-012af0d19084 · outbound

This paper cites Wasserstein Auto-Encoders.

A Comprehensive Survey on Imbalanced Data Learning Wasserstein Auto-Encoders

Reference 26

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Observation 8603c25b-bf73-4566-8c38-51d4efbf1931 · outbound

This paper cites Ewgan: Entropy-based wasserstein gan for imbalanced learning,.

A Comprehensive Survey on Imbalanced Data Learning Ewgan: Entropy-based wasserstein gan for imbalanced learning,

Reference 27

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Observation b5cd251e-3d3a-43f8-9e03-47631540f618 · outbound

This paper cites Wasserstein generative adversarial networks,.

A Comprehensive Survey on Imbalanced Data Learning Wasserstein generative adversarial networks,

Reference 28

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Observation f78a2ba9-4117-4947-b0c8-d2f33a029952 · outbound

This paper cites Eid-gan: Generative adversarial nets for extremely imbalanced data augmentation,.

A Comprehensive Survey on Imbalanced Data Learning Eid-gan: Generative adversarial nets for extremely imbalanced data augmentation,

Reference 29

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Observation e3dacf16-fea1-44bf-8766-b85d530435a1 · outbound

This paper cites Smotified-gan for class imbal- anced pattern classification problems,.

A Comprehensive Survey on Imbalanced Data Learning Smotified-gan for class imbal- anced pattern classification problems,

Reference 30

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Observation f6cb6d48-542d-48e3-a99b-eb8d27b40fe5 · outbound

This paper cites Generative adversarial minority oversampling,.

A Comprehensive Survey on Imbalanced Data Learning Generative adversarial minority oversampling,

Reference 31

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Observation b274d40b-d084-4e98-9188-cda13a47eb07 · outbound

This paper cites Denoising diffusion probabilistic models,.

A Comprehensive Survey on Imbalanced Data Learning Denoising diffusion probabilistic models,

Reference 32

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This paper cites Semantic image synthesis via diffusion models,.

A Comprehensive Survey on Imbalanced Data Learning Semantic image synthesis via diffusion models,

Reference 33

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This paper cites DiffMix: Diffusion Model-based Data Synthesis for Nuclei Segmentation and Classification in Imbalanced Pathology Image Datasets.

A Comprehensive Survey on Imbalanced Data Learning DiffMix: Diffusion Model-based Data Synthesis for Nuclei Segmentation and Classification in Imbalanced Pathology Image Datasets

Reference 34

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This paper cites Diffusion Augmentation for Sequential Recommendation.

A Comprehensive Survey on Imbalanced Data Learning Diffusion Augmentation for Sequential Recommendation

Reference 35

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This paper cites MosaicFusion: Diffusion Models as Data Augmenters for Large Vocabulary Instance Segmentation.

A Comprehensive Survey on Imbalanced Data Learning MosaicFusion: Diffusion Models as Data Augmenters for Large Vocabulary Instance Segmentation

Reference 36

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This paper cites PoGDiff: Product-of-Gaussians Diffusion Models for Imbalanced Text-to-Image Generation.

A Comprehensive Survey on Imbalanced Data Learning PoGDiff: Product-of-Gaussians Diffusion Models for Imbalanced Text-to-Image Generation

Reference 37

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Observation 6b8d8100-5c9f-42f8-85ac-c12f6b3d1363 · outbound

This paper cites Training Class-Imbalanced Diffusion Model Via Overlap Optimization.

A Comprehensive Survey on Imbalanced Data Learning Training Class-Imbalanced Diffusion Model Via Overlap Optimization

Reference 38

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This paper cites Latent-based diffusion model for long-tailed recognition,.

A Comprehensive Survey on Imbalanced Data Learning Latent-based diffusion model for long-tailed recognition,

Reference 39

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This paper cites Rethinking noise sampling in class-imbalanced diffusion models,.

A Comprehensive Survey on Imbalanced Data Learning Rethinking noise sampling in class-imbalanced diffusion models,

Reference 40

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This paper cites Addressing the curse of imbalanced training sets: one-sided selection,.

A Comprehensive Survey on Imbalanced Data Learning Addressing the curse of imbalanced training sets: one-sided selection,

Reference 41

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This paper cites The condensed nearest neighbor rule (corresp.),.

A Comprehensive Survey on Imbalanced Data Learning The condensed nearest neighbor rule (corresp.),

Reference 42

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Observation 63a62452-ae2c-45e4-b6b1-fb415f8b9826 · outbound

This paper cites An experiment with the edited nearest-neighbor rule,.

A Comprehensive Survey on Imbalanced Data Learning An experiment with the edited nearest-neighbor rule,

Reference 43

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Observation a90a7597-2a4c-4069-bf12-b654f196bdd7 · outbound

This paper cites Improving identification of difficult small classes by balancing class distribution,.

A Comprehensive Survey on Imbalanced Data Learning Improving identification of difficult small classes by balancing class distribution,

Reference 44

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This paper cites Knn model-based approach in classification,.

A Comprehensive Survey on Imbalanced Data Learning Knn model-based approach in classification,

Reference 45

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This paper cites knn approach to unbalanced data distributions: a case study involving information extraction,.

A Comprehensive Survey on Imbalanced Data Learning knn approach to unbalanced data distributions: a case study involving information extraction,

Reference 46

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This paper cites Neighbourhood-based under- sampling approach for handling imbalanced and overlapped data,.

A Comprehensive Survey on Imbalanced Data Learning Neighbourhood-based under- sampling approach for handling imbalanced and overlapped data,

Reference 47

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This paper cites Cluster-based under-sampling approaches for imbalanced data distributions,.

A Comprehensive Survey on Imbalanced Data Learning Cluster-based under-sampling approaches for imbalanced data distributions,

Reference 48

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This paper cites Cluster-based majority under-sampling approaches for class imbalance learning,.

A Comprehensive Survey on Imbalanced Data Learning Cluster-based majority under-sampling approaches for class imbalance learning,

Reference 49

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Observation 56b9e360-5ac9-40d5-868e-b1facf91ce24 · outbound

This paper cites Clustering-based undersampling in class-imbalanced data,.

A Comprehensive Survey on Imbalanced Data Learning Clustering-based undersampling in class-imbalanced data,

Reference 50

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This paper cites Diversified sensitivity-based undersampling for imbalance classification problems,.

A Comprehensive Survey on Imbalanced Data Learning Diversified sensitivity-based undersampling for imbalance classification problems,

Reference 51

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

A Comprehensive Survey on Imbalanced Data Learning Fast-cbus: A fast clustering-based undersampling method for addressing the class im- balance problem,

Reference 52

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This paper cites Evolutionary undersampling for classifica- tion with imbalanced datasets: Proposals and taxonomy,.

A Comprehensive Survey on Imbalanced Data Learning Evolutionary undersampling for classifica- tion with imbalanced datasets: Proposals and taxonomy,

Reference 53

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This paper cites Evolutionary undersampling for imbalanced big data classification,.

A Comprehensive Survey on Imbalanced Data Learning Evolutionary undersampling for imbalanced big data classification,

Reference 54

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

A Comprehensive Survey on Imbalanced Data Learning Eusc: A clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification,

Reference 55

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This paper cites Eusboost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling,.

A Comprehensive Survey on Imbalanced Data Learning Eusboost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling,

Reference 56

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Observation 017c2536-26e6-4806-8f9d-4d2cb80da185 · outbound

This paper cites Evolutionary undersampling boosting for imbalanced classification of breast cancer malignancy,.

A Comprehensive Survey on Imbalanced Data Learning Evolutionary undersampling boosting for imbalanced classification of breast cancer malignancy,

Reference 57

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Observation 07d59496-9582-475d-8534-7270f2c180fa · outbound

This paper cites Trainable undersampling for class-imbalance learning,.

A Comprehensive Survey on Imbalanced Data Learning Trainable undersampling for class-imbalance learning,

Reference 58

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Observation 28a2a1de-d633-4f45-8b76-116cf9b162f9 · outbound

This paper cites Spatial distribution-based imbalanced undersampling,.

A Comprehensive Survey on Imbalanced Data Learning Spatial distribution-based imbalanced undersampling,

Reference 59

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Observation 0e49d643-7ee7-4665-a915-76225538c0fe · outbound

This paper cites Relevant information un- dersampling to support imbalanced data classification,.

A Comprehensive Survey on Imbalanced Data Learning Relevant information un- dersampling to support imbalanced data classification,

Reference 60

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Observation 9e1d958f-b250-4878-862d-d5b004205c0d · outbound

This paper cites Entropy and confidence-based un- dersampling boosting random forests for imbalanced problems,.

A Comprehensive Survey on Imbalanced Data Learning Entropy and confidence-based un- dersampling boosting random forests for imbalanced problems,

Reference 61

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Observation 629de183-8c4c-4c38-ba45-63338256d745 · outbound

This paper cites A study of the behavior of several methods for balancing machine learning training data,.

A Comprehensive Survey on Imbalanced Data Learning A study of the behavior of several methods for balancing machine learning training data,

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A Comprehensive Survey on Imbalanced Data Learning A cluster-based hybrid sampling approach for imbalanced data classification,

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

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

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

A Comprehensive Survey on Imbalanced Data Learning Smote–ipf: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering,

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A Comprehensive Survey on Imbalanced Data Learning An ensemble imbalanced classification method based on model dynamic selection driven by data partition hybrid sampling,

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This paper cites Bbn: Bilateral-branch network with cumulative learning for long-tailed visual recognition,.

A Comprehensive Survey on Imbalanced Data Learning Bbn: Bilateral-branch network with cumulative learning for long-tailed visual recognition,

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A Comprehensive Survey on Imbalanced Data Learning The devil is in classification: A simple framework for long- tail instance segmentation,

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

A Comprehensive Survey on Imbalanced Data Learning Long-tailed multi-label visual recognition by collaborative training on uniform and re-balanced samplings,

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A Comprehensive Survey on Imbalanced Data Learning Overcoming classifier imbalance for long-tail object detection with balanced group softmax,

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This paper cites Learning from multiple experts: Self- paced knowledge distillation for long-tailed classification,.

A Comprehensive Survey on Imbalanced Data Learning Learning from multiple experts: Self- paced knowledge distillation for long-tailed classification,

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This paper cites Ace: Ally complementary experts for solving long-tailed recognition in one-shot,.

A Comprehensive Survey on Imbalanced Data Learning Ace: Ally complementary experts for solving long-tailed recognition in one-shot,

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This paper cites Reslt: Residual learning for long-tailed recognition,.

A Comprehensive Survey on Imbalanced Data Learning Reslt: Residual learning for long-tailed recognition,

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A Comprehensive Survey on Imbalanced Data Learning Self-supervised aggregation of diverse experts for test-agnostic long-tailed recognition,

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This paper cites Potential anchoring for imbalanced data classification,.

A Comprehensive Survey on Imbalanced Data Learning Potential anchoring for imbalanced data classification,

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This paper cites Constructing balance from imbal- ance for long-tailed image recognition,.

A Comprehensive Survey on Imbalanced Data Learning Constructing balance from imbal- ance for long-tailed image recognition,

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This paper cites Ehso: Evolutionary hybrid sampling in overlapping scenarios for imbalanced learning,.

A Comprehensive Survey on Imbalanced Data Learning Ehso: Evolutionary hybrid sampling in overlapping scenarios for imbalanced learning,

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A Comprehensive Survey on Imbalanced Data Learning Dynamic sampling in convolutional neural networks for imbalanced data clas- sification,

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A Comprehensive Survey on Imbalanced Data Learning Rethinking the value of labels for improving class-imbalanced learning,

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This paper cites Crest: A class- rebalancing self-training framework for imbalanced semi-supervised learning,.

A Comprehensive Survey on Imbalanced Data Learning Crest: A class- rebalancing self-training framework for imbalanced semi-supervised learning,

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

A Comprehensive Survey on Imbalanced Data Learning Sar: Self-adaptive refinement on pseudo labels for multiclass-imbalanced semi-supervised learning,

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A Comprehensive Survey on Imbalanced Data Learning Daso: Distribution-aware semantics-oriented pseudo-label for imbalanced semi-supervised learn- ing,

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A Comprehensive Survey on Imbalanced Data Learning Active learning for class imbalance problem,

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A Comprehensive Survey on Imbalanced Data Learning Active learning with extreme learning machine for online imbalanced multiclass classifica- tion,

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A Comprehensive Survey on Imbalanced Data Learning Active learning for word sense disambiguation with methods for addressing the class imbalance problem,

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This paper cites Minority class oriented active learning for imbalanced datasets,.

A Comprehensive Survey on Imbalanced Data Learning Minority class oriented active learning for imbalanced datasets,

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A Comprehensive Survey on Imbalanced Data Learning A comprehensive active learning method for multiclass imbalanced data streams with concept drift,

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This paper cites A cost-sensitive active learning for imbalance data with uncertainty and diversity combination,.

A Comprehensive Survey on Imbalanced Data Learning A cost-sensitive active learning for imbalance data with uncertainty and diversity combination,

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This paper cites Deep active learning models for imbalanced image classification,.

A Comprehensive Survey on Imbalanced Data Learning Deep active learning models for imbalanced image classification,

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This paper cites Similarity-based active learning for image classification under class imbalance,.

A Comprehensive Survey on Imbalanced Data Learning Similarity-based active learning for image classification under class imbalance,

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

A Comprehensive Survey on Imbalanced Data Learning Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm,

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This paper cites Thresholding for making classifiers cost- sensitive,.

A Comprehensive Survey on Imbalanced Data Learning Thresholding for making classifiers cost- sensitive,

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A Comprehensive Survey on Imbalanced Data Learning Metacost: A general method for making classifiers cost- sensitive,

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A Comprehensive Survey on Imbalanced Data Learning Training cost-sensitive neural networks with methods addressing the class imbalance problem,

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A Comprehensive Survey on Imbalanced Data Learning On multi-class cost-sensitive learning,

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A Comprehensive Survey on Imbalanced Data Learning Class-balanced loss based on effective number of samples,

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