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Linguistic fuzzy rules in data mining: follow-up Mamdani fuzzy modeling principle

By  and A. FernándezF. Herrera and A. Fernández

Abstract

Abstract. From the definition of fuzzy sets by Zadeh in 1965, fuzzy logic has become a significant area of interest for researchers on artificial intelligence. In particular, Professor Mamdani was the pioneer who investigated the use of fuzzy logic for interpreting the human derived control rules, and therefore his work was considered a milestone application of this theory. In this work, we aim to carry out an overview of the principles of fuzzy modeling given by Mamdani and its application to different areas of data mining that can be exploited such as classification, association rule mining or subgroup discovery, among others. Specifically, we present a case of study on classification with highly imbalanced data-sets in which linguistic fuzzy rule based systems have shown to achieve a good behaviour among other techniques such as decision trees

Topics: Mamdani Fuzzy Rule Based Systems, Fuzzy Logic, Linguistic fuzzy partitions, Data Mining, Classification, Association rule mining, Subgroup discovery, Imbalanced data-sets
Publisher: Springer
Year: 2014
OAI identifier: oai:CiteSeerX.psu:10.1.1.415.2120
Provided by: CiteSeerX
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