Mining axiomatic fuzzy set association rules for classification problems

Xin Wang, Xiaodong Liu, Witold Pedrycz, Xiaolei Zhu and Guangfei Hu

European Journal of Operational Research, 2012, vol. 218, issue 1, 202-210

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.

Keywords: Data mining; Fuzzy association rules; AFS fuzzy logic; Knowledge acquisition; Classification (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:218:y:2012:i:1:p:202-210

DOI: 10.1016/j.ejor.2011.04.022

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