An instance-weighting method to induce cost-sensitive trees

We introduce an instance-weighting method to induce cost-sensitive trees. 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 provides insufficient evidence to support the idea 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 data sets. The instance-weighting method is simpler and more effective in implementation than a previous method based on altered priors.