Learning Through Utility Optimization in Regression Tasks - Branco, Paula; Torgo, Luis; Ribeiro, Rita P.; Frank, Eibe; Pfahringer, Bernhard; Rau, Markus Michael (2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2017)
Accounting for misclassification costs is important in many practical applications of machine learning, and cost-sensitive techniques for classification have been studied extensively. Utility-based learning provides a generalization of purely cost-based approaches that considers both costs and benefits, enabling application to domains with complex cost-benefit settings. However, there is little work on utility- or cost-based learning for regression. In this paper, we formally define the problem of utility-based regression and propose a strategy for maximizing the utility of regression models. We verify our findings in a large set of experiments that show the advantage of our proposal in a diverse set of domains, learning algorithms and cost/benefit settings.