Title of Paper:Mining axiomatic fuzzy set association rules for classification problems

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Date of Publication:2012-04-01

Journal:EUROPEAN JOURNAL OF OPERATIONAL RESEARCH

Included Journals:SCIE、EI、Scopus

Volume:218

Issue:1

Page Number:202-210

ISSN No.:0377-2217

Key Words:Data mining; Fuzzy association rules; AFS fuzzy logic; Knowledge acquisition; Classification

Abstract:In this paper, we propose a novel method to mine association rules for classification problems namely AFSRC (AFS association rules for classification) realized in the framework of the axiomatic fuzzy set (AFS) theory. This model provides a simple and efficient rule generation mechanism. It can also retain meaningful rules for imbalanced classes by fuzzifying the concept of the class support of a rule. In addition, AFSRC can handle different data types occurring simultaneously. Furthermore, the new model can produce membership functions automatically by processing available data. An extensive suite of experiments are reported which offer a comprehensive comparison of the performance of the method with the performance of some other methods available in the literature. The experimental result shows that AFSRC outperforms most of other methods when being quantified in terms of accuracy and interpretability. AFSRC forms a classifier with high accuracy and more interpretable rule base of smaller size while retaining a sound balance between these two characteristics. (C) 2011 Elsevier B.V. All rights reserved.