Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning

Guillaume Lemaitre (1) , Fernando Nogueira (2) , Christos K Aridas (3)

1 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
2 ShoppeAI
3 University of Patras

imbalanced-learn is an open-source python toolbox aiming at providing a wide range of methods to cope with the problem of imbalanced dataset frequently encountered in machine learning and pattern recognition. The implemented state-of-the-art methods can be categorized into 4 groups: (i) under-sampling, (ii) over-sampling, (iii) combination of over-and under-sampling, and (iv) ensemble learning methods. The proposed toolbox depends only on numpy, scipy, and scikit-learn and is distributed under MIT license. Furthermore , it is fully compatible with scikit-learn and is part of the scikit-learn-contrib supported project. Documentation, unit tests as well as integration tests are provided to ease usage and contribution. Source code, binaries, and documentation can be downloaded from https://github.com/scikit-learn-contrib/imbalanced-learn.

Mots clés: Imbalanced Dataset; Over-Sampling; Under-Sampling; Ensemble Learning; Machine Learning; Python

Domaines: Traitement du signal et de l'image [eess.SP]; Machine Learning [stat.ML]

Guillaume Lemaitre, Fernando Nogueira, Christos K Aridas. Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning. Journal of Machine Learning Research, 2017, 18, pp.1 - 5. ⟨hal-01516244⟩