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Received: 2005-07-08

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Journal of Zhejiang University SCIENCE A 2006 Vol.7 No.4 P.602~606

10.1631/jzus.2006.A0602


Immune algorithm for discretization of decision systems in rough set theory


Author(s):  Jia Ping, Dai Jian-hua, Chen Wei-dong, Pan Yun-he, Zhu Miao-liang

Affiliation(s):  Institute of Artificial Intelligence, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   jhdai@zju.edu.cn

Key Words:  Rough sets, Discretization, Immune algorithm, Decision system


Jia Ping, Dai Jian-hua, Chen Wei-dong, Pan Yun-he, Zhu Miao-liang. Immune algorithm for discretization of decision systems in rough set theory[J]. Journal of Zhejiang University Science A, 2006, 7(19): 602~606.

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author="Jia Ping, Dai Jian-hua, Chen Wei-dong, Pan Yun-he, Zhu Miao-liang",
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publisher="Zhejiang University Press & Springer",
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%A Chen Wei-dong
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%A Zhu Miao-liang
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T1 - Immune algorithm for discretization of decision systems in rough set theory
A1 - Jia Ping
A1 - Dai Jian-hua
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A1 - Pan Yun-he
A1 - Zhu Miao-liang
J0 - Journal of Zhejiang University Science A
VL - 7
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SP - 602
EP - 606
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Y1 - 2006
PB - Zhejiang University Press & Springer
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Abstract: 
Rough set theory plays an important role in knowledge discovery, but cannot deal with continuous attributes, thus discretization is a problem which we cannot neglect. And discretization of decision systems in rough set theory has some particular characteristics. Consistency must be satisfied and cuts for discretization is expected to be as small as possible. Consistent and minimal discretization problem is NP-complete. In this paper, an immune algorithm for the problem is proposed. The correctness and effectiveness were shown in experiments. The discretization method presented in this paper can also be used as a data pretreating step for other symbolic knowledge discovery or machine learning methods other than rough set theory.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1] Chun, K.S., Jung, H.K., Yahn, S.Y., 1997. Shape optimization of electromagnetic devices using immune algorithm. IEEE Tran. Magnetics, 33(2):1876-1879.

[2] Chun, J.S., Jung, H.K., Yahn, S.Y., 1998. A study on comparison of optimizing performances between immune algorithm and other heuristic algorithms. IEEE Tran. Magnetics, 34(5):2972-2975.

[3] Dai, J.H., 2004a. A Genetic Algorithm for Discretization of Decision Systems. Proceedings of the 3rd International Conference on Machine Learning and Cybernetics, IEEE Press, New Jersey, p.1319-1323.

[4] Dai, J.H., 2004b. Structure of Rough Approximations Based on Molecular Lattices. Proceedings of the 4th International Conference on Rough Sets and Current Trends in Computing (RSCTC2004), LNAI 3066. Uppsala, Sweden, p.69-77.

[5] Dai, J.H., 2005. Logic for Rough Sets with Rough Double Stone Algebraic Semantics. Proceedings of the Tenth International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC 2005), LNAI 3641, Regina, Canada, p.141-148.

[6] Dai, J.H., Li, Y.X., 2002. Study on Discretization Based on Rough Set Theory. Proceedings of the First International Conference on Machine Learning and Cybernetics, IEEE Press, New Jersey, p.1371-1373.

[7] Dai, J.H., Chen, W.D., Pan, Y.H., 2004. A minimal axiom group of rough set based on quasi-ordering. Journal of Zhejiang University SCIENCE, 5(7):810-815.

[8] Nguyen, S.H., 1997. Discretization of Real Value Attributes: Boolean Reasoning Approach. Ph.D Thesis, Warsaw University, Poland.

[9] Nguyen, S.H., 1998. Discretization Problems for Rough Set Methods. In: Polkowski, L., Skowron, A.(Eds.), Proceedings of the First International Conference on Rough Sets and Current Trend in Computing (RSCTC’98), Lecture Notes on Artificial Intelligence, Springer-Verlag, Berlin, 1424:545-552.

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