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

Abstract : 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.
Complete list of metadatas

Cited literature [22 references]  Display  Hide  Download

https://hal.inria.fr/hal-01516244
Contributor : Guillaume Lemaitre <>
Submitted on : Saturday, April 29, 2017 - 3:06:49 PM
Last modification on : Thursday, June 27, 2019 - 1:36:06 PM
Long-term archiving on : Sunday, July 30, 2017 - 12:22:28 PM

File

16-365.pdf
Publisher files allowed on an open archive

Identifiers

  • HAL Id : hal-01516244, version 1

Citation

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

Share

Metrics

Record views

432

Files downloads

830