About hyperbox-brain

hyperbox-brain is an open-source machine learning package in Python for hyperbox-based machine learning algorithms. Learning algorithms using hyperboxes as fundamental representational and building blocks are a branch of machine learning methods. These algorithms have enormous potential for high scalability and online adaptation of predictors built using hyperbox data representations to the dynamically changing environments. This library focuses on developing and extending the learning algorithms for a specific type of universal hyperbox-based classifiers, i.e., fuzzy min-max neural networks and general fuzzy min-max neural network.

Hyperboxes can be used to deal with the pattern classification and clustering problems effectively by partitioning the pattern space and assigning a class label or cluster associated with a degree of certainty for each region. Each fuzzy min-max hyperbox is represented by minimum and maximum points together with a fuzzy membership function. The membership function is employed to compute the degree-of-fit of each input sample to a given hyperbox. Meanwhile, the hyperboxes are continuously adjusted during the training process to cover the input patterns. The use of hyperboxes for learning systems can form a core module aiming to build smart adaptive systems and life-long learning systems in the near future.

Ecosystem

hyperbox-brain is part of the hyperbox-based machine learning ecosystem. In Python, this library can be used together with pipeline and hyper-parameter optimisers in the scikit-learn library. This library can be also compatible with other optimisers in Python such as hyperopt and Optuna.

Development team

This library is the result of hyperbox-based machine learning project conducted by the Complex Adaptive Systems in the University of Technology Sydney. Current members of the development team (in alphabetical order):

  • Prof. Bogdan Gabrys

  • Dr. Thanh Tung Khuat

We also acknowledge the individual members of the open-source community who have contributed to this project.

Citing

If hyperbox-brain has been useful for your research and you would like to cite it in an academic publication, please use the following paper:

@article{khga23,
    title={hyperbox-brain: A Python toolbox for hyperbox-based machine learning algorithms},
    author={Khuat, Thanh Tung and Gabrys, Bogdan},
    journal={SoftwareX},
    volume={23},
    pages={101425},
    year={2023},
    url={https://doi.org/10.1016/j.softx.2023.101425},
    publisher={Elsevier}
}