hyperbox-brain: A Toolbox for Hyperbox-based Machine Learning Algorithms

TL;DR

hyperbox-brain is an open-source Python library that provides a unified, easy-to-use implementation of hyperbox-based machine learning algorithms, facilitating research, benchmarking, and practical application of these models.

Contribution

It introduces the first comprehensive, user-friendly Python package for hyperbox-based classifiers, compatible with scikit-learn and numpy, supporting research and non-expert usage.

Findings

Provides a unified API for hyperbox algorithms

Enables easy benchmarking and research

Supports online learning and handling missing data

Abstract

Hyperbox-based machine learning algorithms are an important and popular branch of machine learning in the construction of classifiers using fuzzy sets and logic theory and neural network architectures. This type of learning is characterised by many strong points of modern predictors such as a high scalability, explainability, online adaptation, effective learning from a small amount of data, native ability to deal with missing data and accommodating new classes. Nevertheless, there is no comprehensive existing package for hyperbox-based machine learning which can serve as a benchmark for research and allow non-expert users to apply these algorithms easily. hyperbox-brain is an open-source Python library implementing the leading hyperbox-based machine learning algorithms. This library exposes a unified API which closely follows and is compatible with the renowned scikit-learn and numpy toolboxes. The library may be installed from Python Package Index (PyPI) and the conda package manager and is distributed under the GPL-3 license. The source code, documentation, detailed tutorials, and the full descriptions of the API are available at https://uts-caslab.github.io/hyperbox-brain.