关键词: 粗糙集, 测量误差, 代价敏感, 属性选择

Abstract: This paper proposes a cost-sensitive optimal error bound selection approach to address data with measurement errors. The considered misclassification costs are sensitive to both examples and test costs. With this in mind, misclassification costs and test costs are adaptively computed according to error bound. In order to minimize average total cost, this paper designs an algorithm for cost-sensitive optimal error bound selection. The experimental results on four UCI databases show that the designed algorithm can select the feature set with the optimal error bound. The selected feature set leads to the lowest average total cost.

Key words: rough set, measurement error, cost sensitive, feature selection