MetaUtil: Meta Learning for Utility Maximization in Regression

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2018

Autores

Branco, P (Autor) Outra; Torgo, L (Autor) FCUP; Rita Ribeiro (Autor) FCUP

Ata de Conferência Internacional

Título: Discovery Science - 21st International Conference, DS 2018, Limassol, Cyprus, October 29-31, 2018, Proceedings

Páginas: 129-143

21st International Conference on Discovery Science, DS 2018

29 October 2018 through 31 October 2018

Indexação

Scopus - 1 Citação

Outras Informações

ID Authenticus: P-00P-S0N

DOI: 10.1007/978-3-030-01771-2_9

Abstract (EN): Several important real world problems of predictive analytics involve handling different costs of the predictions of the learned models. The research community has developed multiple techniques to deal with these tasks. The utility-based learning framework is a generalization of cost-sensitive tasks that takes into account both costs of errors and benefits of accurate predictions. This framework has important advantages such as allowing to represent more complex settings reflecting the domain knowledge in a more complete and precise way. Most existing work addresses classification tasks with only a few proposals tackling regression problems. In this paper we propose a new method, MetaUtil, for solving utility-based regression problems. The MetaUtil algorithm is versatile allowing the conversion of any out-of-the-box regression algorithm into a utility-based method. We show the advantage of our proposal in a large set of experiments on a diverse set of domains. © 2018, Springer Nature Switzerland AG.

Idioma: Inglês

Tipo (Avaliação Docente): Científica