A Benefit-Cost Based Method for Cost-Sensitive Decision Trees

Cost-sensitive learning is popular during the process of classification. A fundamental issue in decision tree inductive learning is the attribute selection measure at each non-terminal node of the tree. However, existing literatures have not taken the trade-off between cost and benefit into account well. In this paper, we present a new strategy for attributes selection, which is a trade-off method between classification ability and cost-sensitive learning including misclassification costs and test costs with different units, for selecting splitting attributes in cost-sensitive decision trees induction. The experimental results show our method outperform the existed methods in terms of the decrease of misclassification cost.