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      Learning Through Utility Optimization in Regression Tasks

      Branco, Paula; Torgo, Luís; Ribeiro, Rita P.; Frank, Eibe; Pfahringer, Bernhard; Rau, Markus Michael
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      Utility_Optimization_DSAA17Copyright.pdf
      Accepted version, 1.592Mb
      DOI
       10.1109/DSAA.2017.63
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      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
      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.
      Date
      2017
      Type
      Conference Contribution
      Publisher
      IEEE
      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.
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      • Computing and Mathematical Sciences Papers [1455]
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