Learning Through Utility Optimization in Regression Tasks

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

Branco, P (Author) Other; Torgo, L (Author) FCUP; Rita Ribeiro (Author) FCUP; Frank, E (Author) Other; Pfahringer, B (Author) Other; Rau, MM (Author) Other

Conference proceedings International

Title: 2017 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2017, Tokyo, Japan, October 19-21, 2017

Pages: 30-39

Indexing

ISI Web of Knowledge - 6 Citations

Scopus - 3 Citations

Other information

Authenticus ID: P-00N-H19

DOI: 10.1109/dsaa.2017.63

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10