Cost-Sensitive Decision Tree Induction with Label-Dependent Late Constraints.

Completion time requirements are often imposed on a classification task. In practice, the desired completion time for classifying a subject may depend on its label (target) value. For example, a timely diagnosis is important for an illness that requires immediate medical attention. It is common in medical diagnoses, therefore, to set completion times based on the severity level of the illness. In this study, we use “label-dependent" completion time requirements to formulate a new classification problem for cost-sensitive decision tree induction by adding “late constraints" to control the rate of tardy classifications for each label value. Adding the late constraints generalizes and enriches the decision tree induction problem, but also poses a challenge to developing an efficient solution algorithm because the conventional approach based on the “divide-and-conquer" strategy cannot be used. We develop a novel algorithm that relaxes the late constraints and iteratively solves a series of cost-sensitive decision tree problems under systematically-generated late penalties. The results of an extensive numerical experiment show that the proposed algorithm is effective in finding the optimal or a near-optimal solution. [ABSTRACT FROM AUTHOR]