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RSFDGRC
2007
Springer

Constructing Associative Classifier Using Rough Sets and Evidence Theory

9 years 7 months ago
Constructing Associative Classifier Using Rough Sets and Evidence Theory
Constructing accurate classifier based on association rule is an important and challenging task in data mining. In this paper, a novel combination strategy based on rough sets (RST) and evidence theory (DST) for associative classification (RSETAC) is proposed. In RSETAC, rules are regarded as classification experts, after the calculation of the basic probability assignments (bpa) according to rule confidences and evidence weights employing RST, Yang’s rule of combination is employed to combine the distinct evidences to realize an aggregate classification. A numerical example is shown to highlight the procedure of the proposed method. The comparison with popular methods like CBA, C4.5, RIPPER and MCAR indicates that RSETAC is a competitive method for classification based on association rule.
Yuan-Chun Jiang, Ye-Zheng Liu, Xiao Liu, Jie-Kui Z
Added 09 Jun 2010
Updated 09 Jun 2010
Type Conference
Year 2007
Where RSFDGRC
Authors Yuan-Chun Jiang, Ye-Zheng Liu, Xiao Liu, Jie-Kui Zhang
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