FOLD-RM: A Scalable, Efficient, and Explainable Inductive Learning Algorithm for Multi-Category Classification of Mixed Data
FOLD-RM: A Scalable, Efficient, and Explainable Inductive Learning Algorithm for Multi-Category Classification of Mixed Data
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DOI：
10.1017/s1471068422000205
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发表时间：
2022-02
影响因子：
1.4
通讯作者：
Huaduo Wang;Farhad Shakerin;Gopal Gupta
中科院分区：
文献类型：
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作者：
Huaduo Wang;Farhad Shakerin;Gopal Gupta
摘要FOLD-RM是一种自动归纳学习算法，用于学习混合（数值和分类）数据的默认规则。它为多类别分类任务生成（可解释的）答案集编程（ASP）规则集，同时保持效率和可扩展性。FOLD-RM算法在性能上与广泛使用的最先进的算法（如XGBoost和多层感知器）具有竞争力，然而，与这些算法不同，FOLD-RM算法产生可解释的模型。FOLD-RM在某些数据集上优于XGBoost，特别是大型数据集。FOLD-RM还为预测提供了人性化的解释。
Abstract FOLD-RM is an automated inductive learning algorithm for learning default rules for mixed (numerical and categorical) data. It generates an (explainable) answer set programming (ASP) rule set for multi-category classification tasks while maintaining efficiency and scalability. The FOLD-RM algorithm is competitive in performance with the widely used, state-of-the-art algorithms such as XGBoost and multi-layer perceptrons, however, unlike these algorithms, the FOLD-RM algorithm produces an explainable model. FOLD-RM outperforms XGBoost on some datasets, particularly large ones. FOLD-RM also provides human-friendly explanations for predictions.