Cost-Sensitive Attribute Reduction in Decision-Theoretic Rough Set Models

被引：18

作者：

Liao, Shujiao ^{[1
,2
]}

Zhu, Qingxin ^{[1
]}

Min, Fan ^{[3
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu 611731, Peoples R China

[2] Minnan Normal Univ, Sch Math & Stat, Zhangzhou 363000, Peoples R China

[3] Southwest Petr Univ, Sch Comp Sci, Chengdu 610500, Peoples R China

来源：

MATHEMATICAL PROBLEMS IN ENGINEERING | 2014年 / 2014卷

基金：

美国国家科学基金会;

关键词：

Heuristic algorithms;

D O I：

10.1155/2014/875918

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In recent years, the theory of decision-theoretic rough set and its applications have been studied, including the attribute reduction problem. However, most researchers only focus on decision cost instead of test cost. In this paper, we study the attribute reduction problem with both types of costs in decision-theoretic rough set models. A new definition of attribute reduct is given, and the attribute reduction is formulated as an optimization problem, which aims to minimize the total cost of classification. Then both backtracking and heuristic algorithms to the new problem are proposed. The algorithms are tested on four UCI (University of California, Irvine) datasets. Experimental results manifest the efficiency and the effectiveness of both algorithms. This study provides a new insight into the attribute reduction problem in decision-theoretic rough set models.

引用

页数：9