Learning Through Utility Optimization in Regression Tasks

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2017

Autores

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

Ata de Conferência Internacional

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

Páginas: 30-39

Indexação

ISI Web of Knowledge - 6 Citações

Scopus - 3 Citações

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 10