Abstract
Feature selection is an important notions in rough sets. This paper presents a method combining tolerance relation together with rough sets. There is noise data in practical data sets. This paper investigates the feature selection method based on variable precision tolerance rough sets. The parameter was discussed and the parameter interval was described. With the change of the parameter value, the feature selection was different. The efficiency of the proposed method can be illustrated by an experiment with standard dataset from UCI database.
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References
Pawlak, Z.: Rough Sets. International Journal of Information Computer Science 11(5), 341–356 (1982)
Wang, G.Y.: Rough Set Theory and Knowledge Acquisition. Jiaotong University Press, Xi’an (2001) (in Chinese)
Miao, D.Q., Wang, J.: Information-Based Algorithm for Reduction of Knowledge. In: IEEE International Conference on Intelligent Processing Systems, pp. 1155–1158 (1997)
Grzymala-Busse, J.W.: Discretization of Numerical Attributes. In: Klösgen, W., Zytkow, J. (eds.) Handbook of Data Mining and Knowledge Discovery, pp. 218–225. Oxford University Press (2002)
Parthalin, N.M., Shen, Q.: Exploring The Boundary Region of Tolerance Rough Sets for Feature Selection. Pattern Recognition 42, 655–667 (2009)
Ziarko, W.: Variable precision rough set model. Journal of Computer and System Sciences 46, 39–59 (1993)
Zhang, H.Y., Leung, Y., Zhou, L.: Variable-precision-dominance-based rough set approach to interval-valued information systems. Information Sciences 244, 75–272 (2013)
Grzymala-Busse, J.W., Grzymala-Busse, W.J.: Handling Missing Attribute Values. In: Maimon, O., Rokach, L. (eds.) Handbook of Data Mining and Knowledge Discovery, pp. 37–57 (2005)
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© 2014 Springer International Publishing Switzerland
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Jiao, N. (2014). A Feature Seletion Method Based on Variable Precision Tolerance Rough Sets. In: Miao, D., Pedrycz, W., Ślȩzak, D., Peters, G., Hu, Q., Wang, R. (eds) Rough Sets and Knowledge Technology. RSKT 2014. Lecture Notes in Computer Science(), vol 8818. Springer, Cham. https://doi.org/10.1007/978-3-319-11740-9_46
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DOI: https://doi.org/10.1007/978-3-319-11740-9_46
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11739-3
Online ISBN: 978-3-319-11740-9
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