Abstract
The attributes in rough set must be discretized, but the general theory on discretization did not think about the decision attribute adequately during discretization of data, as a result, it leads to several redundant rules and lower calculation efficiency. The discretization method of continuous attributes based on decision attributes which is discussed in this paper gives more attention to both significance of attributes and the decision attributes. The continuous attributes are discretized in sequence according to their significance. The result shows less breakpoints and higher recognition accuracy. The experiment on database Iris for UCI robot learning validates the feasibility of our method. Comparing the result with documents [6] and [11], the method given in this paper shows higher recognition accuracy and much less breakpoints.
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References
Pawlak, Z.: Rough sets. International Journal of Information and Computer Science 11(5), 341–356 (1982)
Li, Y.M., Zhu, S.J., Chen, X.H., et al.: Data mining model based on rough set theory. J. T. Singhua Univ. (Sci. & Tech.) 39(1), 110–113 (1999)
Yan, M.: Approximate Reduction Based on Conditional Information. Acta Electronica Sinica 35(11), 2156–2160 (2007)
Wang, G.Y.: Rough set theory and knowledge acquisition. Xi’ an Jiaotong University Press, Xi’an (2001)
Jiang, S.Y., Li, X., Zheng, Q.: Approximate equal frequency discretization method. Journal of Jinan University (Natural Science) 30(1), 31–34 (2009)
Peng, J.W., Qin, J.W.: Improved heuristic algorithm for discretization. Computer Engineering and Design 29(15), 4003–4005 (2008)
Bai, G.Z., Pei, Z.L., Wan, J., et al.: Attribute discretization method based on rough set theory and information entropy. Application Research of Computers 25(6), 1701–1703 (2008)
Zhang, L., Lu, X.Y., Wu, H.Y., et al.: Heuristic algorithm used in attribute value reduction of rough set. Chinese Journal of Scientific Instrument 30(1), 82–84 (2009)
Wang, F., Liu, D.Y., Xue, W.X.: Discretizing Continuous Variables of Bayesian Networks Based on genetic Algorithms. Chinese J. Computers 25(8), 794–800 (2002)
Choi, Y.S., Moon, B.R., Seo, S.Y.: Genetic Fuzzy Discretization with Adaptive intervals for Classification Problems. In: Proceedings of the Genetic and Evolutionary Computation Conference, Washington, DC, USA, pp. 2037–2043 (2005)
Xia, Z.G., Xia, S.X., Niu, Q., Zhang, L.: Method of discretization of continuous attributes based on improved genetic algorithm. Computer Engineering and Design 29(16), 4275–4279 (2008)
Chen, G.: Discretization method of continuous attributes in decision table based on genetic algorithm. Chinese Journal of Scientific Instrument 28(9), 1700–1705 (2007)
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Sun, Y., Ren, Z., Zhou, T., Zhai, Y., Pu, D. (2010). Discretization Method of Continuous Attributes Based on Decision Attributes. In: Wang, F.L., Deng, H., Gao, Y., Lei, J. (eds) Artificial Intelligence and Computational Intelligence. AICI 2010. Lecture Notes in Computer Science(), vol 6320. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16527-6_46
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DOI: https://doi.org/10.1007/978-3-642-16527-6_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16526-9
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