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Learning Through Utility Optimization in Regression Tasks

Abstract
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.
Type
Conference Contribution
Type of thesis
Series
Citation
Branco, P., Torgo, L., Ribeiro, R. P., Frank, E., Pfahringer, B., & Rau, M. M. (2017). Learning Through Utility Optimization in Regression Tasks. In Proceeding of 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA) (pp. 30–39). Washington, DC, USA: IEEE. https://doi.org/10.1109/DSAA.2017.63
Date
2017
Publisher
IEEE
Degree
Supervisors
Rights
© 2017 IEEE. This is the author's version of the work. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.