A Heuristic Genetic Algorithm for Continuous Attribute Discretization in Rough Set Theory


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Continuous attribute discretization based on rough set is to got possibly minimum number of cuts, and at the same time it should not weaken the indiscernibility ability of the original decision system. In order to obtain the optimal cut set of the continuous attribute system, based on research the choice of candidate cut set, this paper presents a heuristic genetic algorithm for continuous attribute discretization to decision tables. In this algorithm making the importance of the continuous cut as heuristic message, a new operator is constructed to not only maintain the discernibility of the cuts selected, but also improve local search ability of the algorithm. Compared the performance of this method with the others’, this method is proved effective and superiority.



Advanced Materials Research (Volumes 211-212)

Edited by:

Ran Chen




Z. H. Ren et al., "A Heuristic Genetic Algorithm for Continuous Attribute Discretization in Rough Set Theory", Advanced Materials Research, Vols. 211-212, pp. 132-136, 2011

Online since:

February 2011




[1] Pawlak Z.Cybern Syst, Vol. 29(1998), p.661-688.

[2] Wang Guoying. Rough Set Theory and Knowledge Acquisition, (Xi 'an Jiaotong University Press. 2001).

[3] Li Minqiang, Kou Jisong, Lin Dan. Fundamental Theory of Genetic Algorithms and its Application, (Science Press, China 2002).

[4] Nguyen H S. Discretization of Real Value Attributes: Boolean reason-ing Approach, (Warsaw University Warsaw, Poland 1997).

[5] Fayyad U M. Branching on attribute values in decision tree generation. (Proc of AAA I- 94 . Cambridge, MA: The AAA I Press, 1994. 601-606).

[6] Zhao Weidong, Dai Weihui. Systems Engineering-theory & Practice. Vol. 19(2003),P. 62-67.

[7] Gong Dunwei, Pan Fengping. The Theory and Research of Adaptive Genetic Alforithm. (China University of Mining And Technology Press. China 2003).