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

Version 2 2024-06-04, 06:38
Version 1 2019-03-08, 09:44
journal contribution
posted on 2024-06-04, 06:38 authored by YZ Liu, YC Jiang, Xiao LiuXiao Liu, SL Yang
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. © 2008 Elsevier B.V. All rights reserved.

History

Journal

Knowledge-based systems

Volume

21

Pagination

786-793

Location

Amsterdam, The Netherlands

ISSN

0950-7051

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2008, Elsevier B.V.

Issue

8

Publisher

Elsevier

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