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<p><b>Abstract</b>—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—<it>minimum error</it> trees or <it>minimum high cost error</it> 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.</p>
Cost-sensitive, decision trees, induction, greedy divide-and-conquer algorithm, instance weighting

K. Ting, "An Instance-Weighting Method to Induce Cost-Sensitive Trees," in IEEE Transactions on Knowledge & Data Engineering, vol. 14, no. , pp. 659-665, 2002.
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