Prodding the ROC Curve: Constrained Optimization of Classifier Performance

When designing a two-alternative classifier, one ordinarily aims to maximize the classifier's ability to discriminate between members of the two classes. We describe a situation in a real-world business application of machine-learning prediction in which an additional constraint is placed on the nature of the solution: thatthe classifier achieve a specified correct acceptance or correct rejection rate (i.e., that it achieve a fixed accuracy on members of one class or the other). Our domain is predicting churn in the telecommunications industry. Churn refers to customers who switch from one service provider to another. We propose fouralgorithms for training a classifier subject to this domain constraint, and present results showing that each algorithm yields a reliable improvement in performance.