Learning Through Utility Optimization in Regression Tasks

• Published in: 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA) pp. 30 - 39
• Main Authors: Branco, Paula, Torgo, Luis, Ribeiro, Rita P., Frank, Eibe, Pfahringer, Bernhard, Rau, Markus Michael
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2017
• Subjects: Computer science; Electronic mail; Measurement; Optimization; Predictive models; Surface treatment
• DOI: 10.1109/DSAA.2017.63

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.