Analyzing rough set based attribute reductions by extension rule

被引：7
作者：
Li, Bing [1 ]
Chow, Tommy W. S. [1 ]
Tang, Peng [1 ]
机构：
[1] City Univ Hong Kong, Dept Elect Engn, Kowloon, Hong Kong, Peoples R China
来源：
NEUROCOMPUTING | 2014年 / 123卷
关键词：
Attribute reduction; Extension rule; Reduction distribution; Rough set; SUPERVISED FEATURE-SELECTION; MUTUAL INFORMATION;
D O I：
10.1016/j.neucom.2013.07.006
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
An improved discernibility function for rough set based attribute reduction is defined to keep discernibility ability and remove redundant attributes without the precondition of the Positive Region. On the basis of discernibility function, the solution of rough set based attribute reduction can be found by satisfiability methods. With extension rule theory, a satisfiability method, the distribution of solutions with different number of attributes is obtained without enumerating all attribute reductions. Then, it is easy to search the attribute reduction with the smallest number of attributes. In addition, the cost of space and time is analyzed to find factors playing role in the computation of the method. (C) 2013 Elsevier B.V. All rights reserved.
引用
收藏
页码：185 / 196
页数：12
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