CSMC: a combination strategy for multi-class classification based on multiple association rules
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journal contribution
posted on 2024-06-04, 06:38 authored by YZ Liu, YC Jiang, Xiao LiuXiao Liu, SL YangConstructing 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. © 2008 Elsevier B.V. All rights reserved.
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Journal
Knowledge-based systemsVolume
21Pagination
786-793Location
Amsterdam, The NetherlandsISSN
0950-7051Language
engPublication classification
C1.1 Refereed article in a scholarly journalCopyright notice
2008, Elsevier B.V.Issue
8Publisher
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