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

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...

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Bibliographic Details
Published in:Mathematical problems in engineering 2014-01, Vol.2014 (2014), p.1-9
Main Authors: Liao, Shujiao, Zhu, Qingxin, Min, Fan
Format: Article
Language:English
Subjects:
Algorithms
Classification
Computational efficiency
Cost engineering
Debt cancellation
Decision making
Engineering
Game theory
Heuristic
Heuristic methods
Mathematical models
Net operating losses
Optimization
Reduction
Rough set models
Software
Studies
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Summary: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.
ISSN:1024-123X
1563-5147
DOI:10.1155/2014/875918