CSMC: A combination strategy for multi-class classification based on multiple association rules

Authors:
Ye-Zheng Liu;Yuan-Chun Jiang;Xiao Liu;Shan-Lin Yang
Affiliations:
Institute of Electronic Commerce, School of Management, Hefei University of Technology, 193 TunXi Road, Hefei, Anhui Province 230009, China and Key Laboratory of Process Optimization and Intellige ...;Institute of Electronic Commerce, School of Management, Hefei University of Technology, 193 TunXi Road, Hefei, Anhui Province 230009, China and Key Laboratory of Process Optimization and Intellige ...;Faculty of Information and Communication Technologies, Swinburne University of Technology, 3122 Melbourne, Australia;Institute of Electronic Commerce, School of Management, Hefei University of Technology, 193 TunXi Road, Hefei, Anhui Province 230009, China and Key Laboratory of Process Optimization and Intellige ...
Venue:
Knowledge-Based Systems
Year:
2008

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Abstract

Constructing accurate classifier based on association rules is an important and challenging task in data mining and knowledge discovery. In this paper, a novel combination strategy for multi-class classification (CSMC) based on multiple rules is proposed. In CSMC, rules are regarded as classification experts, after the calculation of the basic probability assignments (bpa) and evidence weights, Yang's rule of combination is employed to combine the distinct evidence bodies to realize an aggregate classification. A numerical example is shown to highlight the procedure of the proposed method at the end of this paper. The comparison with popular methods like CBA, C4.5, RIPPER and MCAR indicates that CSMC is a competitive method for classification based on association rule.