IMBENS: Ensemble Class-imbalanced Learning in Python (2111.12776v2)

Abstract: 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.

• The paper introduces IMBENS, a comprehensive toolbox implementing 14 ensemble methods to tackle class imbalance in machine learning.
• It leverages a scikit-learn-inspired API for ease of integration and extensibility, streamlining model training with customizable resampling and logging.
• IMBENS enhances predictive accuracy in imbalanced datasets across domains like medical diagnostics and fraud detection, promoting collaborative research.

IMBENS: Ensemble Class-imbalanced Learning in Python

The paper presents IMBENS, a Python-based open-source toolbox designed for ensemble class-imbalanced learning (EIL). This tool addresses the pervasive challenge of class imbalance in machine learning tasks, where certain classes are underrepresented, leading to biased models and degraded predictive performance.

IMBENS focuses on leveraging ensemble learning techniques to mitigate class imbalance problems by integrating well-established methods such as resampling-based and reweighting-based solutions. The toolbox offers a comprehensive implementation of 14 popular EIL methods, including techniques like SMOTEBoost, BalanceCascade, and AdaCost.

Key Contributions

1. Comprehensive Method Implementation: IMBENS includes a wide array of EIL models, surpassing existing tools in scope. Each method is developed with high-level abstractions to facilitate ease of use and extensibility, allowing researchers to create new models without extensive prior configuration.
2. User-friendly API: Modeled closely after scikit-learn's API, IMBENS ensures ease of adoption for users familiar with existing Python machine learning libraries. This design choice enhances accessibility and accelerates integration into existing workflows.
3. Enhanced Flexibility: Additional features such as customizable resampling schedules and detailed logging are provided. These enhancements empower users to meticulously control model training and evaluation processes.
4. Open-source Collaboration: Distributed under the MIT license, IMBENS invites contributions from the research community to further its development. Its active presence on GitHub demonstrates robust engagement, with documented contribution guidelines encouraging participation.
5. Robust Documentation and Testing: Supporting materials developed using sphinx and numpydoc ensure comprehensive guidance for users. A high test coverage of 96% facilitates reliability and stability across various applications.

Implications and Future Directions

The deployment of IMBENS has significant implications for both research and practical applications. By addressing class imbalance more effectively, predictive models become more accurate across domains like medical diagnostics and fraud detection where imbalances are common. The modular design further supports experimentation and benchmarking of new algorithms, fostering innovation in the imbalanced learning space.

Future development of IMBENS is poised to incorporate advanced techniques such as evolutionary algorithms, meta-learning, and hybrid sampling strategies. Further emphasis on detailed documentation and user support materials is also planned, enhancing usability for researchers and practitioners.

In summary, IMBENS contributes a sophisticated toolset to the field of class-imbalanced learning, emphasizing extensibility, ease of use, and collaborative potential. Its adoption and continued development represent a forward step in addressing a persistent challenge in machine learning.