Abstract
Metric technique has recently been applied to solve such data mining problems as classification, clustering, feature selection, decision tree construction. In this paper, we apply metric technique to solve a attribute reduction problem of incomplete decision tables in rough set theory. We generalize Liang entropy in incomplete information systems and investigate its properties. Based on the generalized Liang entropy, we establish a metric between coverings and study its properties for attribute reduction. Consequently, we propose a metric based attribute reduction method in incomplete decision tables and perform experiments on UCI data sets. The experimental results show that metric technique is an effective method for attribute reduction in incomplete decision tables.
The authors are supported by grants 2011/01/B/ST6/03867 from the Polish National Science Centre (NCN), and the grant SP/I/1/77065/10 in frame of the strategic scientific research and experimental development program: “Interdisciplinary System for Interactive Scientific and Scientific-Technical Information” founded by the Polish National Centre for Research and Development (NCBiR).
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Nguyen, L.G., Nguyen, H.S. (2013). Metric Based Attribute Reduction in Incomplete Decision Tables. In: Ciucci, D., Inuiguchi, M., Yao, Y., Ślęzak, D., Wang, G. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2013. Lecture Notes in Computer Science(), vol 8170. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41218-9_11
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DOI: https://doi.org/10.1007/978-3-642-41218-9_11
Publisher Name: Springer, Berlin, Heidelberg
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