Immune algorithm for discretization of decision systems in rough set theory

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.

[1]  Yuan-Xiang Li,et al.  Study on discretization based on rough set theory , 2002, Proceedings. International Conference on Machine Learning and Cybernetics.

[2]  J. Chun,et al.  Shape optimization of electromagnetic devices using immune algorithm , 1997 .

[3]  Hung Son Nguyen,et al.  Discretization Problem for Rough Sets Methods , 1998, Rough Sets and Current Trends in Computing.

[4]  Yun-He Pan,et al.  A minimal axiom group for rough set based on quasi-ordering , 2004, Journal of Zhejiang University. Science.

[5]  Jian-Hua Dai.  A genetic algorithm for discretization of decision systems , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[6]  Jianhua Dai,et al.  Logic for Rough Sets with Rough Double Stone Algebraic Semantics , 2005, RSFDGrC.

[7]  Jianhua Dai.  Structure of Rough Approximations Based on Molecular Lattices , 2004, Rough Sets and Current Trends in Computing.

[8]  Song-Yop Hahn,et al.  A study on comparison of optimization performances between immune algorithm and other heuristic algorithms , 1998 .