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
Permanent Research Commons link: https://hdl.handle.net/10289/12515
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.
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