Abstract
In this paper, a novel feature selection method of discernibility object pair set is provided. At first, the feature selection definition of new method is presented. What’s more, it is proved that the above feature selection definition is equal to the feature selection definition based on conditional information entropy. In order to compute discernibility object pair set, a quick algorithm for simplified decision system is introduced, whose time complexity is O(|C ∥ U |). On this condition, an efficient and novel algorithm based on discernibility object pair set for feature selection in conditional information entropy model is designed, whose time and space complexity are O(|C ∥ U |) + o(|c ∥ u |c |2) and O(|U |C |2) + O(|U |) respectively. At last, an example is employed to illustrate the efficiency of the new algorithm.
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References
Pawlak, Z., Skowron, A.: Rudiments of rough sets. Information Science 117(1), 3–37 (2007)
Pawlak, Z., Wong, S.K., Ziarko, W.: Rough Sets: Probabilistic versus deterministic approach. Computational Intelligence 29(4), 81–95 (1988)
Zhang, W.X., Qiu, G.F., Wu, W.Z.: General theory of attribute reduction in rough set. Science in China Series E 35(12), 1304–1313 (2005)
Li, Y.S., Jia, R.Y.: Coverage Reduction algorithm based on conditional information entropy. Computer Engineering 36(16), 176–179 (2010)
Liu, Q.H., Li, F., Min, F.: An efficient knowledge reduction algorithm based on new conditional information entropy. Control and Decision 20(8), 878–882 (2005)
Jiang, Y., Wang, P.: Complete algorithm for attribute reduction based on discernibility matrix. Computer Engineering and Applications 43(19), 185–187 (2007)
Wang, J.Y., Gao, C.: Improved algorithm for attribute reduction based on discernihility matrix. Computer Engineering 35(3), 66–67 (2009)
Zhi, T.Y., Miao, D.Q.: The binary discernibility matrix transformation and high efficiency attributes reduction algorithms conformation. Computer Science 29(2), 140–142 (2003)
Xu, Z.Y., Yang, B.R., Song, W.: Quick algorithm for computing core based on discernibility object pair set. Systems Engineering and Electronics 30(4), 731–734 (2008)
Xu, Z.Y., Yang, B.R., Guo, Y.P.: Quick Algorithm for Computing Core Based on Information Entropy. Journal of Chinese Computer Systems 28(2), 279–282 (2007)
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© 2011 Springer-Verlag Berlin Heidelberg
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Ruan, J., Zhang, C. (2011). A Novel Feature Selection Method for the Conditional Information Entropy Model. In: Deng, H., Miao, D., Wang, F.L., Lei, J. (eds) Emerging Research in Artificial Intelligence and Computational Intelligence. AICI 2011. Communications in Computer and Information Science, vol 237. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24282-3_83
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DOI: https://doi.org/10.1007/978-3-642-24282-3_83
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24281-6
Online ISBN: 978-3-642-24282-3
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