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A novel algorithm for fuzzy rule induction in data mining

Afifi, Ashraf

Authors

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Dr Ashraf Afifi Ashraf.Afifi@uwe.ac.uk
Senior Lecturer in Engineering Management



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
Publication Date Apr 1, 2014
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
APA6 Citation Afifi, A. (2014). A novel algorithm for fuzzy rule induction in data mining. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 228(5), 877-895. https://doi.org/10.1177/0954406213492273
DOI https://doi.org/10.1177/0954406213492273
Keywords fuzzy rule induction, fuzzy rules evaluation, fuzzy discretisation, fuzzy inference, membership functions, machine fault diagnosis
Publisher URL http://dx.doi.org/10.1177/0954406213492273
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