Immune algorithm for discretization of decision systems in rough set theory

被引：5

作者：

Jia P. ^{[1
]}

Dai J.-H. ^{[1
]}

Chen W.-D. ^{[1
]}

Pan Y.-H. ^{[1
]}

Zhu M.-L. ^{[1
]}

机构：

[1] Institute of Artificial Intelligence, Zhejiang University

来源：

Journal of Zhejiang University-SCIENCE A | 2006年 / 7卷 / 4期

基金：

中国博士后科学基金;

关键词：

Decision system; Discretization; Immune algorithm; Rough sets;

D O I：

10.1631/jzus.2006.A0602

中图分类号：

学科分类号：

摘要：

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.

引用

页码：602 / 606

页数：4

共 9 条

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