Constructing Associative Classifier Using Rough Sets and Evidence Theory

Authors:
Yuan-Chun Jiang;Ye-Zheng Liu;Xiao Liu;Jie-Kui Zhang
Affiliations:
Institute of Electronic Commerce, Hefei University of Technology, 230009 Hefei, China;Institute of Electronic Commerce, Hefei University of Technology, 230009 Hefei, China;Institute of Electronic Commerce, Hefei University of Technology, 230009 Hefei, China;Institute of Electronic Commerce, Hefei University of Technology, 230009 Hefei, China
Venue:
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Year:
2009

Citing 6
Cited 0

C4.5: programs for machine learning

C4.5: programs for machine learning
Variable precision rough set model

Journal of Computer and System Sciences
Combining belief functions when evidence conflicts

Decision Support Systems
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules

ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
CAEP: Classification by Aggregating Emerging Patterns

DS '99 Proceedings of the Second International Conference on Discovery Science
MCAR: multi-class classification based on association rule

AICCSA '05 Proceedings of the ACS/IEEE 2005 International Conference on Computer Systems and Applications

Quantified Score

Hi-index	0.01

Visualization

Abstract

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.