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Toolbox for imbalanced dataset in machine learning.

Project description

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.

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Documentation

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

Installation

Dependencies

imbalanced-learn is tested to work under Python 2.7 and Python 3.5.

  • scipy(>=0.17.0)

  • numpy(>=1.10.4)

  • scikit-learn(>=0.17.1)

Installation

imbalanced-learn is currently available on the PyPi’s reporitories and you can install it via pip:

pip install -U imbalanced-learn

The package is release also in Anaconda Cloud platform:

conda install -c glemaitre 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
python setup.py install

Testing

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

make coverage

About

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 addresing 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 links [1]

    3. Under-sampling with Cluster Centroids

    4. NearMiss-(1 & 2 & 3) [2]

    5. Condensend Nearest Neighbour [3]

    6. One-Sided Selection [4]

    7. Neighboorhood Cleaning Rule [5]

    8. Edited Nearest Neighbours [6]

    9. Instance Hardness Threshold [7]

    10. Repeated Edited Nearest Neighbours [14]

  • Over-sampling
    1. Random minority over-sampling with replacement

    2. SMOTE - Synthetic Minority Over-sampling Technique [8]

    3. bSMOTE(1 & 2) - Borderline SMOTE of types 1 and 2 [9]

    4. SVM SMOTE - Support Vectors SMOTE [10]

    5. ADASYN - Adaptive synthetic sampling approach for imbalanced learning [15]

  • Over-sampling followed by under-sampling
    1. SMOTE + Tomek links [12]

    2. SMOTE + ENN [11]

  • Ensemble sampling
    1. EasyEnsemble [13]

    2. BalanceCascade [13]

The different algorithms are presented in the following notebook.

This is a work in progress. Any comments, suggestions or corrections are welcome.

References:

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