hyperbox-brain documentation
Hyperbox-Brain
A scikit-learn compatible hyperbox-based machine learning library in Python.
Introduction
hyperbox-brain is a Python open source toolbox implementing hyperbox-based machine learning algorithms built on top of scikit-learn and is distributed under the GPL-3.0 license.
The project was started in 2018 by Prof. Bogdan Gabrys and Dr. Thanh Tung Khuat at the Complex Adaptive Systems Lab - The University of Technology Sydney. This project is a core module aiming to the formulation of explainable life-long learning systems in near future.
If you use hyperbox-brain, please use this BibTeX entry:
@article{khgb22,
author = {Thanh Tung Khuat and Bogdan Gabrys},
title = {Hyperbox-brain: A Python Toolbox for Hyperbox-based Machine Learning Algorithms},
journal = {ArXiv},
pages = {1-7},
year = 2022,
url = {https://hyperbox-brain.readthedocs.io/en/latest/}
}
User Guides
- Installation
- Features
- Types of input variables
- Incremental learning
- Agglomerative learning
- Ensemble learning
- Multigranularity learning
- Learning from both labelled and unlabelled data
- Ability to directly process missing data
- Continual learning ability of new classes
- Data editing and pruning approaches
- Scikit-learn compatible estimators
- Explainability of predicted results
- Easy to use
- Jupyter notebooks
- Available models
- Quickstart
- Contributing
- About hyperbox-brain
API Reference
If you are looking for information on a specific function, class or method, this part of the documentation is for you.
Hyperbox-brain API
- utilitity functions
- base
- mixed-data learners
- batch learners
- ensemble learners
- ensemble_learner.base_bagging
- ensemble_learner.base_cross_val_bagging
- ensemble_learner.decision_comb_bagging
- ensemble_learner.decision_comb_cross_val_bagging
- ensemble_learner.model_comb_bagging
- ensemble_learner.model_comb_cross_val_bagging
- ensemble_learner.random_hyperboxes
- ensemble_learner.cross_val_random_hyperboxes
- incremental learners
- multigranular learners
Tutorials
- Tutorials
- Batch learners
- Incremental learners
- Original Online Learning Algorithm for GFMM
- Improved Online Learning Algorithm for GFMM
- Fuzzy Min-Max Neural Network with Original Online Learning Algorithm
- Enhanced Online Learning Algorithm for FMNN
- Enhanced Online Learning Algorithm with K-nearest Hyperboxes Selection for FMNN
- Refined Online Learning Algorithm for FMNN
- Multigranular learners
- Ensemble learners
- Decision-level Bagging of Hyperbox-based Models
- Decision-level Bagging of Hyperbox-based Models with Hyper-parameter Optimisation
- Model-level Bagging of Hyperbox-based Models
- Model-level Bagging of Hyperbox-based Learners with Hyper-parameter Optimisation
- Random Hyperboxes
- Random Hyperboxes with Hyper-parameter Optimisation for Base Learners
- Mixed data learners
- Integration with sklearn pipeline
- Integration with sklearn hyperparameter optimisation
- Other learning abilities of GFMM models
- Store and load the trained models