/imbalanced-learn

A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning

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imbalanced-learn

imbalanced-learn is a python package offering a number of re-sampling techniques commonly used in datasets showing strong between-class imbalance. It is compatible with scikit-learn and is part of scikit-learn-contrib projects.

Documentation

Installation documentation, API documentation, and examples can be found on the documentation.

Installation

Dependencies

imbalanced-learn is tested to work under Python 3.6+. The dependency requirements are based on the last scikit-learn release:

  • scipy(>=0.19.1)
  • numpy(>=1.13.3)
  • scikit-learn(>=0.22)
  • joblib(>=0.11)
  • keras 2 (optional)
  • tensorflow (optional)

Additionally, to run the examples, you need matplotlib(>=2.0.0) and pandas(>=0.22).

Installation

imbalanced-learn is currently available on the PyPi's repository and you can install it via `pip`:

pip install -U imbalanced-learn

The package is release also in Anaconda Cloud platform:

conda install -c conda-forge imbalanced-learn

If you prefer, you can clone it and run the setup.py file. Use the following commands to get a copy from GitHub and install all dependencies:

git clone https://github.com/scikit-learn-contrib/imbalanced-learn.git
cd imbalanced-learn
pip install .

Or install using pip and GitHub:

pip install -U git+https://github.com/scikit-learn-contrib/imbalanced-learn.git

Testing

After installation, you can use pytest to run the test suite:

make coverage

Development

The development of this scikit-learn-contrib is in line with the one of the scikit-learn community. Therefore, you can refer to their Development Guide.

About

If you use imbalanced-learn in a scientific publication, we would appreciate citations to the following paper:

@article{JMLR:v18:16-365,
author  = {Guillaume  Lema{{\^i}}tre and Fernando Nogueira and Christos K. Aridas},
title   = {Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning},
journal = {Journal of Machine Learning Research},
year    = {2017},
volume  = {18},
number  = {17},
pages   = {1-5},
url     = {http://jmlr.org/papers/v18/16-365}
}

Most classification algorithms will only perform optimally when the number of samples of each class is roughly the same. Highly skewed datasets, where the minority is heavily outnumbered by one or more classes, have proven to be a challenge while at the same time becoming more and more common.

One way of addressing this issue is by re-sampling the dataset as to offset this imbalance with the hope of arriving at a more robust and fair decision boundary than you would otherwise.

Re-sampling techniques are divided in two categories:
  1. Under-sampling the majority class(es).
  2. Over-sampling the minority class.
  3. Combining over- and under-sampling.
  4. Create ensemble balanced sets.

Below is a list of the methods currently implemented in this module.

  • Under-sampling
    1. Random majority under-sampling with replacement
    2. Extraction of majority-minority Tomek links1
    3. Under-sampling with Cluster Centroids
    4. NearMiss-(1 & 2 & 3)2
    5. Condensed Nearest Neighbour3
    6. One-Sided Selection4
    7. Neighboorhood Cleaning Rule5
    8. Edited Nearest Neighbours6
    9. Instance Hardness Threshold7
    10. Repeated Edited Nearest Neighbours8
    11. AllKNN9
  • Over-sampling
    1. Random minority over-sampling with replacement
    2. SMOTE - Synthetic Minority Over-sampling Technique10
    3. SMOTENC - SMOTE for Nominal Continuous11
    4. bSMOTE(1 & 2) - Borderline SMOTE of types 1 and 212
    5. SVM SMOTE - Support Vectors SMOTE13
    6. ADASYN - Adaptive synthetic sampling approach for imbalanced learning14
    7. KMeans-SMOTE15
  • Over-sampling followed by under-sampling
    1. SMOTE + Tomek links16
    2. SMOTE + ENN17
  • Ensemble classifier using samplers internally
    1. Easy Ensemble classifier18
    2. Balanced Random Forest19
    3. Balanced Bagging
    4. RUSBoost20
  • Mini-batch resampling for Keras and Tensorflow

The different algorithms are presented in the sphinx-gallery.

References:

  1. : I. Tomek, “Two modifications of CNN,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 6, pp. 769-772, 1976.↩

  2. I. Mani, J. Zhang. “kNN approach to unbalanced data distributions

    A case study involving information extraction,” In Proceedings of the Workshop on Learning from Imbalanced Data Sets, pp. 1-7, 2003.

    ↩

  3. : P. E. Hart, “The condensed nearest neighbor rule,” IEEE Transactions on Information Theory, vol. 14(3), pp. 515-516, 1968.↩

  4. M. Kubat, S. Matwin, “Addressing the curse of imbalanced training sets

    One-sided selection,” In Proceedings of the 14th International Conference on Machine Learning, vol. 97, pp. 179-186, 1997.

    ↩

  5. : J. Laurikkala, “Improving identification of difficult small classes by balancing class distribution,” Proceedings of the 8th Conference on Artificial Intelligence in Medicine in Europe, pp. 63-66, 2001.↩

  6. : D. Wilson, “Asymptotic Properties of Nearest Neighbor Rules Using Edited Data,” IEEE Transactions on Systems, Man, and Cybernetrics, vol. 2(3), pp. 408-421, 1972.↩

  7. : M. R. Smith, T. Martinez, C. Giraud-Carrier, “An instance level analysis of data complexity,” Machine learning, vol. 95(2), pp. 225-256, 2014.↩

  8. : I. Tomek, “An experiment with the edited nearest-neighbor rule,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 6(6), pp. 448-452, 1976.↩

  9. : I. Tomek, “An experiment with the edited nearest-neighbor rule,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 6(6), pp. 448-452, 1976.↩

  10. N. V. Chawla, K. W. Bowyer, L. O. Hall, W. P. Kegelmeyer, “SMOTE

    Synthetic minority over-sampling technique,” Journal of Artificial Intelligence Research, vol. 16, pp. 321-357, 2002.

    ↩
  11. N. V. Chawla, K. W. Bowyer, L. O. Hall, W. P. Kegelmeyer, “SMOTE

    Synthetic minority over-sampling technique,” Journal of Artificial Intelligence Research, vol. 16, pp. 321-357, 2002.

    ↩
  12. H. Han, W.-Y. Wang, B.-H. Mao, “Borderline-SMOTE

    A new over-sampling method in imbalanced data sets learning,” In Proceedings of the 1st International Conference on Intelligent Computing, pp. 878-887, 2005.

    ↩

  13. : H. M. Nguyen, E. W. Cooper, K. Kamei, “Borderline over-sampling for imbalanced data classification,” In Proceedings of the 5th International Workshop on computational Intelligence and Applications, pp. 24-29, 2009.↩

  14. H. He, Y. Bai, E. A. Garcia, S. Li, “ADASYN

    Adaptive synthetic sampling approach for imbalanced learning,” In Proceedings of the 5th IEEE International Joint Conference on Neural Networks, pp. 1322-1328, 2008.

    ↩

  15. : Felix Last, Georgios Douzas, Fernando Bacao, "Oversampling for Imbalanced Learning Based on K-Means and SMOTE"↩

  16. G. E. A. P. A. Batista, A. L. C. Bazzan, M. C. Monard, “Balancing training data for automated annotation of keywords

    A case study,” In Proceedings of the 2nd Brazilian Workshop on Bioinformatics, pp. 10-18, 2003.

    ↩

  17. : G. E. A. P. A. Batista, R. C. Prati, M. C. Monard, “A study of the behavior of several methods for balancing machine learning training data,” ACM Sigkdd Explorations Newsletter, vol. 6(1), pp. 20-29, 2004.↩

  18. : X.-Y. Liu, J. Wu and Z.-H. Zhou, “Exploratory undersampling for class-imbalance learning,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 39(2), pp. 539-550, 2009.↩

  19. C. Chao, A. Liaw, and L. Breiman. "Using random forest to learn imbalanced data." University of California, Berkeley 110 (2004)

    1-12.

    ↩
  20. Seiffert, C., Khoshgoftaar, T. M., Van Hulse, J., & Napolitano, A. "RUSBoost

    A hybrid approach to alleviating class imbalance." IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 40.1 (2010): 185-197.

    ↩