One-benefit learning: Cost-sensitive learning with restricted cost information

Abstract

This paper presents a new formulation for cost-sensitive learning that we call the One-Benefit formulation. Instead of having the correct label for each training example as in the standard classifier learning formulation, in this formulation we have one possible label for each example (which may not be the correct one) and the benefit (or cost) associated with that label. The goal of learning in this formulation is to find the classifier that maximizes the expected benefit of the labelling using only these examples. We present a reduction from One-Benefit learning to standard classifier learning that allows us to use any existing error-minimizing classifier learner to maximize the expected benefit in this formulation by correctly weighting the examples. We also show how to evaluate a classifier using test examples for which we only the benefit for one of the labels. We present preliminary experimental results using a synthetic data generator that allows us to test both our learning method and our evaluation method. Copyright 2005 ACM.