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A fuzzy rule induction algorithm for discovering classification rules

Afifi, Ashraf


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Dr Ashraf Afifi
Senior Lecturer in Engineering Management


© 2016 - IOS Press and the authors. All rights reserved. Recently, the topic of data mining has attracted considerable attention from both academia and industry. Data mining is the process of extracting useful knowledge from large amounts of data. Among the types of knowledge to be mined, classification knowledge is the most widely exploited in engineering applications. A variety of methods exist for inductive learning of crisp classification knowledge. This paper presents a new inductive learning algorithm called FuzzyRULES that extracts fuzzy classification rules from a database of examples. The use of fuzzy sets and fuzzy logic methods not only provides a powerful, flexible approach to handling vagueness and uncertainty, but also increases the expressive power and comprehensibility of the induced classification knowledge. An example involving the induction of process planning rules is used to illustrate the operation of FuzzyRULES. The algorithm has also been compared against conventional crisp and fuzzy rule induction algorithms on several benchmark data sets. The results obtained have shown that FuzzyRULES induces more compact and more accurate classification rule sets.


Afifi, A. (2016). A fuzzy rule induction algorithm for discovering classification rules. Journal of Intelligent and Fuzzy Systems, 30(6), 3067-3085.

Journal Article Type Article
Acceptance Date Jan 1, 2016
Publication Date Apr 30, 2016
Deposit Date Feb 2, 2017
Journal Journal of Intelligent and Fuzzy Systems
Print ISSN 10641246
Electronic ISSN 1875-8967
Publisher IOS Press
Peer Reviewed Peer Reviewed
Volume 30
Issue 6
Pages 3067-3085
Keywords fuzzy sets, rule induction, inductive learning, classification, data mining
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