imbalanced-learn-extra 0.2.10

An implementation of novel oversampling algorithms.

imbalanced-learn-extra

Category	Tools
Development
Package
Documentation
Communication

Introduction

imbalanced-learn-extra is a Python package that extends imbalanced-learn. It implements algorithms that are not included in imbalanced-learn due to their novelty or lower citation number. The current version includes the following:

• A general interface for clustering-based oversampling algorithms.

• The Geometric SMOTE algorithm. It is a geometrically enhanced drop-in replacement for SMOTE, that handles numerical as well as categorical features.

Installation

For user installation, imbalanced-learn-extra is currently available on the PyPi's repository, and you can install it via pip:

pip install imbalanced-learn-extra

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

git clone https://github.com/georgedouzas/imbalanced-learn-extra.git
cd imbalanced-learn-extra
pdm install

SOM clusterer requires optional dependencies:

pip install imbalanced-learn-extra[som]

Usage

All the classes included in imbalanced-learn-extra 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 oversamplers implement a fit method to learn from X and y:

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)

Citing imbalanced-learn-extra

Publications using clustering-based oversampling:

• 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.

Publications using Geometric-SMOTE:

• Douzas, G., Bacao, B. (2019). Geometric SMOTE: a geometrically enhanced drop-in replacement for SMOTE. Information Sciences, 501, 118-135. https://doi.org/10.1016/j.ins.2019.06.007

• Fonseca, J., Douzas, G., Bacao, F. (2021). Increasing the Effectiveness of Active Learning: Introducing Artificial Data Generation in Active Learning for Land Use/Land Cover Classification. Remote Sensing, 13(13), 2619. https://doi.org/10.3390/rs13132619

• Douzas, G., Bacao, F., Fonseca, J., Khudinyan, M. (2019). Imbalanced Learning in Land Cover Classification: Improving Minority Classes’ Prediction Accuracy Using the Geometric SMOTE Algorithm. Remote Sensing, 11(24), 3040. https://doi.org/10.3390/rs11243040

User Support

If you encounter a bug, have a question, or would like to request a new feature, you can get support through the project’s GitHub issue tracker.

• Report a bug: Open a new issue and describe the problem, including steps to reproduce it and your environment details.
• Request a feature: Open a new issue describing the functionality you’d like to see added.
• Ask a question or request help: Use the Q&A discussion board for general usage questions or clarifications.

Before opening a new issue, please check the existing issues to see if your question or problem has already been addressed.