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IMBENS: Ensemble Class-imbalanced Learning in Python

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arxiv 2111.12776 v2 pith:6N52KD3T submitted 2021-11-24 cs.LG cs.AI

Zhining Liu , Jian Kang , Hanghang Tong , Yi Chang This is my paper · ORCID
classification cs.LGcs.AI
keywords ensemblelearningimbenspythonunderimbalanced-ensemblemethodsopen-source
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imbalanced-ensemble, abbreviated as imbens, is an open-source Python toolbox for leveraging the power of ensemble learning to address the class imbalance problem. It provides standard implementations of popular ensemble imbalanced learning (EIL) methods with extended features and utility functions. These ensemble methods include resampling-based, e.g., under/over-sampling, and reweighting-based, e.g., cost-sensitive learning. Beyond the implementation, we empower EIL algorithms with new functionalities like customizable resampling scheduler and verbose logging, thus enabling more flexible training and evaluating strategies. The package was developed under a simple, well-documented API design that follows scikit-learn for increased ease of use. imbens is released under the MIT open-source license and can be installed from Python Package Index (PyPI) or https://github.com/ZhiningLiu1998/imbalanced-ensemble.

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