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.
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He, H., Min, F. (2011). Accumulated Cost Based Test-Cost-Sensitive Attribute Reduction. In: Kuznetsov, S.O., Ślęzak, D., Hepting, D.H., Mirkin, B.G. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2011. Lecture Notes in Computer Science(), vol 6743. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21881-1_39
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DOI: https://doi.org/10.1007/978-3-642-21881-1_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21880-4
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