FOLD-RM: A Scalable, Efficient, and Explainable Inductive Learning Algorithm for Multi-Category Classification of Mixed Data

Autor:	HUADUO WANG, FARHAD SHAKERIN, GOPAL GUPTA
Rok vydání:	2022
Předmět:	FOS: Computer and information sciences Computer Science - Machine Learning Computational Theory and Mathematics Artificial Intelligence Hardware and Architecture Software Machine Learning (cs.LG) Theoretical Computer Science
Zdroj:	Theory and Practice of Logic Programming. 22:658-677
ISSN:	1475-3081 1471-0684
DOI:	10.1017/s1471068422000205
Popis:	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 (MLPs), 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. Paper presented at the 38th International Conference on Logic Programming (ICLP 2022), 16 pages
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::40d31d9b4160a11e716e082a7a5c2e74 https://doi.org/10.1017/s1471068422000205 Zobrazit plný text záznamu