FOLD-RM: A Scalable, Efficient, and Explainable Inductive Learning Algorithm for Multi-Category Classification of Mixed Data. Issue 5 (1st September 2022)

• Record Type: Journal Article
• Authors: WANG, HUADUO; SHAKERIN, FARHAD; GUPTA, GOPAL
• Is Part Of: Theory and practice of logic programming. Volume 22:Issue 5(2022)
• Page Start: 658
• Page End: 677
• Publication Date: 2022-09-01
• Subjects: explainable AI -- data mining -- inductive logic programming -- machine learning; Logic programming -- Periodicals; Artificial intelligence -- Computer programs -- Periodicals; Constraint programming (Computer science) -- Periodicals; 005.115
• DOI: 10.1017/S1471068422000205
• Languages: English
• ISSNs: 1471-0684

Abstract

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.