Accumulated cost based test-cost-sensitive attribute reduction

Authors:
Huaping He;Fan Min
Affiliations:
School of Computer Science, Sichuan University of Science and Engineering, Zigong, China;Key Lab of Granular Computing, Zhangzhou Normal University, Zhangzhou, China
Venue:
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
Year:
2011

Citing 4
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Abstract

As a generalization of the classical reduct problem, test-costsensitive attribute reduction aims at finding a minimal test-cost reduct. The performance of an existing algorithm is not satisfactory, partly because that the test-cost of an attribute is not appropriate to adjust the attribute significance. In this paper, we propose to use the test-cost sum of selected attributes instead and obtain a new attribute significance function, with which a new algorithm is designed. Experimental results on the Zoo dataset with various test-cost settings show performance improvement of the new algorithm over the existing one.