关键词: 决策粗糙集, 代价敏感, 属性约简, 误分类代价, 测试代价

Abstract: This paper proposes a cost-sensitive classification method by combining cost-sensitive learning with decision theoretic rough set model. In the proposed classification method, the optimal test attribute sets for each test sample are respectively computed based on decision-theoretic rough set and attribute reduction, on which the sample is predicated with the lowest misclassification cost and test cost. Then the optimal classification label for each test sample is determined based on the optimal test attribute set. In view of the high computational complexity in searching global optimal test attribute set, the paper presents a heuristic algorithm to search local optimal test attribute set, in which the contribution of an attribute to decrease the total classification cost is taken as the heuristic function, and the local optimal classification label is determined based on the local optimal test attribute set. The experimental results on UCI database show that the proposed algorithm can effectively decrease the total cost of classification and the test attributes, which leads to a lower misclassification cost and a lower test cost.

Key words: decision-theoretic rough set, cost-sensitive, attribute reduction, misclassification cost, test cost