A novel algorithm for fuzzy rule induction in data mining

Afifi, Ashraf

Abstract

Rule induction as a method of constructing classifiers is of particular interest to data mining because it generates models in the form of If-Then rules which are more expressive and easier for humans to comprehend and check. Several induction algorithms have been developed to learn classification rules. However, most of these algorithms are based on 'crisp' data and produce 'crisp' models. This paper presents FuzzySRI, a novel algorithm based on the techniques of fuzzy sets and fuzzy logic for inducing fuzzy classification rules. The algorithm possesses the clear knowledge representation capability of rule induction methods and the ability of fuzzy techniques to handle vague information. Experimental results show that FuzzySRI can outperform other fuzzy and non-fuzzy learning systems in terms of predictive accuracy, comprehensibility, and computational efficiency. It is also shown that FuzzySRI can be successfully applied to an industrial application concerning the automatic identification of machine faults. © 2013 IMechE.

• Journal Article Type: Article
• Acceptance Date: May 1, 2013
• Online Publication Date: Aug 1, 2013
• Publication Date: Apr 1, 2014
• Deposit Date: Feb 2, 2017
• Journal: Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science
• Print ISSN: 0954-4062
• Electronic ISSN: 2041-2983
• Publisher: SAGE Publications
• Peer Reviewed: Peer Reviewed
• Volume: 228
• Issue: 5
• Pages: 877-895
• DOI: https://doi.org/10.1177/0954406213492273
• Keywords: fuzzy rule induction, fuzzy rules evaluation, fuzzy discretisation, fuzzy inference, membership functions, machine fault diagnosis
• Public URL: https://uwe-repository.worktribe.com/output/819330
• Publisher URL: http://dx.doi.org/10.1177/0954406213492273
• Contract Date: Feb 2, 2017