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Accumulated Cost Based Test-Cost-Sensitive Attribute Reduction

  • Huaping He
  • Fan Min
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6743)

Abstract

As a generalization of the classical reduct problem, test-cost-sensitive 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.

Keywords

Cost-sensitive learning attribute reduction test-cost accumulated cost 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Huaping He
    • 1
  • Fan Min
    • 2
  1. 1.School of Computer ScienceSichuan University of Science and EngineeringZigongChina
  2. 2.Key Lab of Granular ComputingZhangzhou Normal UniversityZhangzhouChina

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