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

Shujiao Liao | Zendy; Qingxin Zhu | Zendy; Fan Min | Zendy

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

Author(s) -

Shujiao Liao,

Qingxin Zhu,

Fan Min

Publication year - 2014

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2014/875918

Subject(s) - rough set , reduct , backtracking , reduction (mathematics) , decision table , heuristic , set (abstract data type) , data mining , decision rule , computer science , dominance based rough set approach , mathematics , mathematical optimization , attribute domain , artificial intelligence , geometry , programming language

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.

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