IMBENS: Ensemble Class-imbalanced Learning in Python
This is an incremental contribution that provides a software package for researchers and practitioners working on imbalanced classification tasks.
The paper introduces IMBENS, an open-source Python toolbox that implements ensemble learning methods to tackle class imbalance problems, providing standard algorithms with extended features like customizable resampling schedulers and scikit-learn-compatible APIs.
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.