Inducing Cost-Sensitive Trees via Instance Weighting

We introduce an instance-weighting method to induce costsensitive trees in this paper. It is a generalization of the standard tree induction process where only the initial instance weights determine the type of tree to be induced—minimum error trees or minimum high cost error trees. We demonstrate that it can be easily adapted to an existing tree learning algorithm. Previous research gave insufficient evidence to support the fact that the greedy divide-and-conquer algorithm can effectively induce a truly cost-sensitive tree directly from the training data. We provide this empirical evidence in this paper. The algorithm incorporating the instance-weighting method is found to be better than the original algorithm in terms of total misclassification costs, the number of high cost errors and tree size in two-class datasets. The instanceweighting method is simpler and more effective in implementation than a previous method based on altered priors.