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awesome-imbalanced-learning
Everything about class-imbalanced/long-tail learning: papers, codes, frameworks, and libraries | 有关类别不平衡/长尾学习的一切：论文、代码、框架与库
https://github.com/ZhiningLiu1998/awesome-imbalanced-learning
Last synced: 1 day ago
JSON representation
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2.1 Surveys
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2.2 Ensemble Learning
- [**Paper**
- [**Code**
- [**Code**
- [**Paper** - learn/scikit-learn/blob/95d4f0841/sklearn/ensemble/_weight_boosting.py#L285)]** - Adaptive Boosting with C4.5
- [**Paper** - Boosting with Data Generation for Imbalanced Data
- [**Paper** - ensemble/blob/main/imbalanced_ensemble/ensemble/over_sampling/smote_bagging.py)]** - Synthetic Minority Over-sampling TEchnique Boosting
- [**Paper** - Modified Synthetic Minority Over-sampling TEchnique Boosting
- [**Paper** - algorithms/blob/master/ramo.py#L133)]** - Ranked Minority Over-sampling in Boosting
- [**Paper** - ensemble/blob/main/imbalanced_ensemble/ensemble/under_sampling/rus_boost.py)]** - Random Under-Sampling Boosting
- [**Paper** - Adaptive Boosting with Negative Correlation Learning
- [**Paper** - Evolutionary Under-sampling in Boosting
- [**Paper** - learn/scikit-learn/blob/95d4f0841/sklearn/ensemble/_bagging.py#L433)]** - Bagging predictor
- [**Paper**
- [**Code**
- [**Code**
- [**Code**
- [**Paper** - ensemble/blob/main/imbalanced_ensemble/ensemble/reweighting/adacost.py)]** - Misclassification Cost-sensitive boosting
- [**Paper** - ensemble/blob/main/imbalanced_ensemble/ensemble/reweighting/adauboost.py)]** - AdaBoost with Unequal loss functions
- [**Paper** - ensemble/blob/main/imbalanced_ensemble/ensemble/reweighting/asymmetric_boost.py)]** - Asymmetric AdaBoost and detector cascade
- [**Paper** - paced-ensemble)][[**Slides**](https://zhiningliu.com/files/ICDE_2020_self_paced_ensemble_slides.pdf)][[**Zhihu/知乎**](https://zhuanlan.zhihu.com/p/86891438)][[**PyPI**](https://pypi.org/project/self-paced-ensemble/)]**
- [**Paper** - paced-ensemble)][[**Slides**](https://zhiningliu.com/files/ICDE_2020_self_paced_ensemble_slides.pdf)][[**Zhihu/知乎**](https://zhuanlan.zhihu.com/p/86891438)][[**PyPI**](https://pypi.org/project/self-paced-ensemble/)]**
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2.3 Data resampling
- [**Code** - Random Over-sampling
- [**Paper** - learn-contrib/imbalanced-learn/blob/master/imblearn/over_sampling/_smote.py#L36)]** - Synthetic Minority Over-sampling TEchnique
- [**Paper** - learn-contrib/imbalanced-learn/blob/master/imblearn/over_sampling/_smote.py#L220)]** - Borderline-Synthetic Minority Over-sampling TEchnique
- [**Paper** - learn-contrib/imbalanced-learn/blob/master/imblearn/over_sampling/_adasyn.py)]** - ADAptive SYNthetic Sampling
- [**Paper** - Selective Preprocessing of Imbalanced Data
- [**Paper** - Mahalanobis Distance-based Over-sampling for *Multi-Class* imbalanced problems.
- [**Code** - Random Under-sampling
- [**Paper** - learn-contrib/imbalanced-learn/blob/master/imblearn/under_sampling/_prototype_selection/_condensed_nearest_neighbour.py)]** - Condensed Nearest Neighbor
- [**Paper** - learn-contrib/imbalanced-learn/blob/master/imblearn/under_sampling/_prototype_selection/_edited_nearest_neighbours.py)]** - Edited Condensed Nearest Neighbor
- [**Paper** - learn-contrib/imbalanced-learn/blob/master/imblearn/under_sampling/_prototype_selection/_tomek_links.py)]** - Tomek's modification of Condensed Nearest Neighbor
- [**Paper** - learn-contrib/imbalanced-learn/blob/master/imblearn/under_sampling/_prototype_selection/_neighbourhood_cleaning_rule.py)]** - Neighborhood Cleaning Rule
- [**Paper** - learn-contrib/imbalanced-learn/blob/master/imblearn/under_sampling/_prototype_selection/_nearmiss.py)]** - Several kNN approaches to unbalanced data distributions.
- [**Paper** - Condensed Nearest Neighbor + TomekLink
- [**Paper** - learn-contrib/imbalanced-learn/blob/master/imblearn/under_sampling/_prototype_selection/_one_sided_selection.py)]** - One Side Selection
- [**Paper** - Evolutionary Under-sampling
- [**Paper** - learn-contrib/imbalanced-learn/blob/master/imblearn/under_sampling/_prototype_selection/_instance_hardness_threshold.py)]** - Instance Hardness Threshold
- [**Paper**
- [**Code**
- [**Code**
- [**Paper** - variants.readthedocs.io/en/latest/_modules/smote_variants/_smote_variants.html#SMOTE_RSB)]** - Hybrid Preprocessing using SMOTE and Rough Sets Theory
- [**Paper** - variants.readthedocs.io/en/latest/_modules/smote_variants/_smote_variants.html#SMOTE_IPF)]** - SMOTE with Iterative-Partitioning Filter
- **smote-variants**
- [**Paper** - Evolutionary Under-sampling
- [**Paper** - learn-contrib/imbalanced-learn/blob/master/imblearn/under_sampling/_prototype_selection/_tomek_links.py)]** - Tomek's modification of Condensed Nearest Neighbor
- [**Paper** - learn-contrib/imbalanced-learn/blob/master/imblearn/over_sampling/_smote.py#L36)]** - Synthetic Minority Over-sampling TEchnique
- [**Paper** - learn-contrib/imbalanced-learn/blob/master/imblearn/under_sampling/_prototype_selection/_instance_hardness_threshold.py)]** - Instance Hardness Threshold
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2.4 Cost-sensitive Learning
- [**Paper** - Cost-sensitive SVMs for highly imbalanced classification
- [**Paper** - Training cost-sensitive neural networks with methods addressing the class imbalance problem.
- [**Paper** - An instance-weighting method to induce cost-sensitive trees
- [**Paper** - An instance-weighting method to induce cost-sensitive trees
- [**Paper** - An instance-weighting method to induce cost-sensitive trees
- [**Paper** - An instance-weighting method to induce cost-sensitive trees
- [**Paper** - An instance-weighting method to induce cost-sensitive trees
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2.5 Deep Learning
- [**Paper**
- [**Paper**
- [**Paper**
- [**Paper** - Song/TAM)]
- [**Paper**
- [**Paper**
- [**Paper** - Park/GraphENS)]
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- [**Paper** - In the later phase of NN training, only do gradient back-propagation for "hard examples" (i.e., with large loss value)
- [**Paper** - A uniform loss function that focuses training on a sparse set of hard examples to prevents the vast number of easy negatives from overwhelming the detector during training.
- [**Paper** - Mean (square) false error that can equally capture classification errors from both the majority class and the minority class.
- [**Paper**
- [**Paper** - Class Rectification Loss for minimizing the dominant effect of majority classes by discovering sparsely sampled boundaries of minority classes in an iterative batch-wise learning process.
- [**Paper** - DRW)] - A theoretically-principled label-distribution-aware margin (LDAM) loss motivated by minimizing a margin-based generalization bound.
- [**Paper** - Compared to Focal Loss, which only down-weights "easy" negative examples, GHM also down-weights "very hard" examples as they are likely to be outliers.
- [**Paper** - balanced-loss)] - a simple and generic class-reweighting mechanism based on Effective Number of Samples.
- [**Paper** - Loss)]
- [**Paper**
- [**Paper** - Loss)]
- [**Paper** - research/learning-to-reweight-examples)] - Implicitly learn a weight function to reweight the samples in gradient updates of DNN.
- [**Paper** - weight-net)] - Explicitly learn a weight function (with an MLP as the function approximator) to reweight the samples in gradient updates of DNN.
- [**Paper** - data-manipulation)]
- [**Paper** - lee/l2b)]
- [**Paper**
- [**Paper**
- [**Paper** - balancing)]
- [**Paper**
- [**Paper** - Rotation)]
- [**Paper** - RIPL/UNO-IC)]
- [**Paper** - research/google-research/tree/master/logit_adjustment)]
- [**Paper** - semi-self)][[**Video**](https://www.youtube.com/watch?v=XltXZ3OZvyI&feature=youtu.be)]
- [**Paper**
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- [**Paper** - oh/daso)]
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- [**Paper** - Nanjing/BBN)]
- [**Paper**
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- [**Paper** - imbalanced-regression)]
- [**Paper** - ren/BalancedMSE)]
- [**Paper** - regression)][[**Video**](https://www.youtube.com/watch?v=grJGixofQRU)]
- J - based-weighting-for-imbalanced-regression)]
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- [**Paper** - balancing)]
- [**Paper** - semi-self)][[**Video**](https://www.youtube.com/watch?v=XltXZ3OZvyI&feature=youtu.be)]
- [**Paper** - Nanjing/BBN)]
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- [**Paper** - regression)][[**Video**](https://www.youtube.com/watch?v=grJGixofQRU)]
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- [**Paper** - effective-ensemble-imbal?s=d3745afc-cfcf-4d60-9f34-63d3d811b55f)]
- [**Paper** - oh/daso)]
- J - based-weighting-for-imbalanced-regression)]
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- [**Paper**
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- [**Paper**
- J - based-weighting-for-imbalanced-regression)]
- J - based-weighting-for-imbalanced-regression)]
- [**Paper** - Class Rectification Loss for minimizing the dominant effect of majority classes by discovering sparsely sampled boundaries of minority classes in an iterative batch-wise learning process.
- J - based-weighting-for-imbalanced-regression)]
- J - based-weighting-for-imbalanced-regression)]
- [**Paper** - research/google-research/tree/master/logit_adjustment)]
- J - based-weighting-for-imbalanced-regression)]
- [**Paper** - effective-ensemble-imbal?s=d3745afc-cfcf-4d60-9f34-63d3d811b55f)]
- J - based-weighting-for-imbalanced-regression)]
- [**Paper** - RIPL/UNO-IC)]
- J - based-weighting-for-imbalanced-regression)]
- [**Paper**
- [**Paper**
- [**Paper** - Compared to Focal Loss, which only down-weights "easy" negative examples, GHM also down-weights "very hard" examples as they are likely to be outliers.
- [**Paper**
- J - based-weighting-for-imbalanced-regression)]
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3.1 Datasets
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3.2 Github Repositories
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3.2.1 *Algorithms & Utilities & Jupyter Notebooks*
- imbalanced-dataset-sampler - A (PyTorch) imbalanced dataset sampler for oversampling low frequent classes and undersampling high frequent ones.
- imbalanced-algorithms - Python-based implementations of algorithms for learning on imbalanced data.
- imbalanced-dataset-sampler - A (PyTorch) imbalanced dataset sampler for oversampling low frequent classes and undersampling high frequent ones.
- class_imbalance - Jupyter Notebook presentation for class imbalance in binary classification.
- Multi-class-with-imbalanced-dataset-classification - Perform multi-class classification on imbalanced 20-news-group dataset.
- Advanced Machine Learning with scikit-learn: Imbalanced classification and text data - Different approaches to feature selection, and resampling methods for imbalanced data.
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3.2.3 *Slides*
- emoji key
- all-contributors
- acm_imbalanced_learning - slides and code for the ACM Imbalanced Learning talk on 27th April 2016 in Austin, TX.
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3.2.2 *Paper list*
- Paper-list-on-Imbalanced-Time-series-Classification-with-Deep-Learning - Imbalanced Time-series Classification
- Anomaly Detection Learning Resources - Anomaly detection related books, papers, videos, and toolboxes.
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Uncategorized
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Uncategorized
- **imbalanced-ensemble** - ensemble)][[**Documentation**](https://imbalanced-ensemble.readthedocs.io/)][[**Gallery**](https://imbalanced-ensemble.readthedocs.io/en/latest/auto_examples/index.html#)][[**Paper**](https://arxiv.org/pdf/2111.12776.pdf)]
- **imbalanced-learn** - learn-contrib/imbalanced-learn)][[**Documentation**](https://imbalanced-learn.org/stable/)][[**Paper**](https://www.jmlr.org/papers/volume18/16-365/16-365.pdf)]
- **smote_variants** - variants.readthedocs.io/en/latest/)][[**Github**](https://github.com/analyticalmindsltd/smote_variants)] - A collection of 85 minority ***over-sampling*** techniques for imbalanced learning with multi-class oversampling and model selection features (All writen in Python, also support R and Julia).
- **caret** - Contains the implementation of Random under/over-sampling.
- **ROSE** - 3)] - Contains the implementation of [ROSE](https://journal.r-project.org/archive/2014-1/menardi-lunardon-torelli.pdf) (Random Over-Sampling Examples).
- **DMwR** - Contains the implementation of [SMOTE](https://arxiv.org/pdf/1106.1813.pdf) (Synthetic Minority Over-sampling TEchnique).
- **KEEL** - SoftComputing-Keel1.0.pdf)] - KEEL provides a simple ***GUI based*** on data flow to design experiments with different datasets and computational intelligence algorithms (***paying special attention to evolutionary algorithms***) in order to assess the behavior of the algorithms. This tool includes many widely used imbalanced learning techniques such as (evolutionary) over/under-resampling, cost-sensitive learning, algorithm modification, and ensemble learning methods.
- API reference
- imbalanced-ensemble - ensemble)][[Documentation](https://imbalanced-ensemble.readthedocs.io/)].
- joblib
- **undersampling** - A Scala library for ***under-sampling and their ensemble variants*** in imbalanced classification.
- scikit-learn - learn-contrib](https://github.com/scikit-learn-contrib) projects.
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Programming Languages
Categories
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Keywords
imbalanced-data
5
machine-learning
4
python
3
classification
3
data-mining
2
data-science
2
imbalanced-learning
2
caching
1
sklearn
1
scikit-learn
1
python3
1
multi-class-classification
1
long-tail
1
imbalanced-classification
1
ensemble-model
1
ensemble-learning
1
ensemble-imbalanced-learning
1
ensemble
1
class-imbalance
1
recognition
1
opensource-management
1
opensource
1
open-source-tooling
1
contributors
1
all-contributors
1
acknowledgements
1
unsupervised-learning
1
time-series-analysis
1
outlier-ensembles
1
outlier-detection
1
outlier
1
graph-neural-networks
1
fraud-detection
1
fraud
1
awesome-list
1
awesome
1
anomaly-detection
1
tutorial
1
pytorch
1
image-classification
1
data-sampling
1
notre-dame
1
smote
1
oversampling
1
undersampling
1
nearest-neighbor-rules
1
imbalance-learning
1
algorithm
1
threading
1
parallel-computing
1