论文标题
HAPEBOX-BRAIN：用于基于HAPEBOX的机器学习算法的工具箱
hyperbox-brain: A Toolbox for Hyperbox-based Machine Learning Algorithms
论文作者
论文摘要
基于HAPYBOX的机器学习算法是使用模糊集和逻辑理论和神经网络体系结构在分类器构建中的机器学习的重要且流行的分支。这种学习的特征是现代预测因素的许多强调，例如高可扩展性，解释性，在线适应性，从少量数据中有效学习，本地处理缺失数据和适应新课程的能力。但是，基于HARPOX的机器学习尚无全面的现有软件包，可以作为研究的基准，并允许非专家用户轻松应用这些算法。 Hyperbox-Brain是一个开源Python库，实现了基于HAPEBOX的机器学习算法。该库公开了一个统一的API，该API紧随其后，并且与著名的Scikit-Learn和Numpy工具箱兼容。可以从Python软件包索引（PYPI）和Conda软件包管理器中安装库，并根据GPL-3许可分发。源代码，文档，详细的教程和API的完整描述可在https://uts-caslab.github.io/hyperbox-brain上获得。
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.
