关键词: 粗糙集, 代价敏感, 属性选择, 动态误分类代价, 自适应分治

Abstract: Cost-sensitive feature selection problem aims at getting an attribute subset with the minimal total cost, through considering the trade-off between test costs and misclassification costs. There are two main challenges in cost-sensitive feature selection problem. On the one hand, most of the cost-sensitive attribute selection methods only take fixed misclassification costs into account, thus these methods can’t solve imbalance class problems. On the other hand, the efficiency is not ideal when dealing with cost-sensitive feature selection on large scale datasets. In this paper, the contributions for the two challenges are summarized as follows. Firstly, it designs a new dynamic mechanism of misclassification costs to minimize total cost. Secondly, each of datasets is adaptively divided according to the scale of the dataset based on divide and conquer method. Finally, cost-sensitive feature selection problem is redefined based on dynamic misclassification costs, and a divide and conquer algorithm is proposed for cost-sensitive feature selection problem. The proposed algorithm is compared with two other algorithms on seven UCI datasets. Some experiments demonstrate that the proposed algorithm can improve the efficiency and obtain the optimal misclassification costs as well, so as to ensure to minimize total cost.

Key words: rough sets, cost-sensitive, feature selection, dynamic misclassification cost, adaptive divide and conquer