Learning Through Utility Optimization in Regression Tasks

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 benef...

Full description

Saved in:
Bibliographic Details
Published in2017 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
LanguageEnglish
Published IEEE 01.10.2017
Subjects
Computer science
Electronic mail
Measurement
Optimization
Predictive models
Surface treatment
Online AccessGet full text
DOI10.1109/DSAA.2017.63

Cover

More Information
Summary: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.
DOI:10.1109/DSAA.2017.63