cluster-over-sampling

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Introduction

A general interface for clustering based over-sampling algorithms.

Installation

For user installation, cluster-over-sampling is currently available on the PyPi's repository, and you can install it via pip:

pip install cluster-over-sampling

Development installation requires to clone the repository and then use PDM to install the project as well as the main and development dependencies:

git clone https://github.com/georgedouzas/cluster-over-sampling.git
cd cluster-over-sampling
pdm install

SOM clusterer requires optional dependencies:

pip install cluster-over-sampling[som]

Usage

All the classes included in cluster-over-sampling follow the imbalanced-learn API using the functionality of the base oversampler. Using scikit-learn convention, the data are represented as follows:

• Input data X: 2D array-like or sparse matrices.
• Targets y: 1D array-like.

The clustering-based oversamplers implement a fit method to learn from X and y:

clustering_based_oversampler.fit(X, y)

They also implement a fit_resample method to resample X and y:

X_resampled, y_resampled = clustering_based_oversampler.fit_resample(X, y)

References

If you use cluster-over-sampling in a scientific publication, we would appreciate citations to any of the following papers:

• G. Douzas, F. Bacao, "Self-Organizing Map Oversampling (SOMO) for imbalanced data set learning", Expert Systems with Applications, vol. 82, pp. 40-52, 2017.
• G. Douzas, F. Bacao, F. Last, "Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE", Information Sciences, vol. 465, pp. 1-20, 2018.
• G. Douzas, F. Bacao, F. Last, "G-SOMO: An oversampling approach based on self-organized maps and geometric SMOTE", Expert Systems with Applications, vol. 183,115230, 2021.