Utility-based regression

• Tipo: Capítulo ou Parte de Livro
• Data: 2007

Autores

Luis Torgo (Autor) FEP; Rita Ribeiro (Autor) Outra

Livro

Título: Knowledge Discovery in Databases: PKDD 2007

Páginas: 597-604

ISBN: 978-3-540-74975-2

ISBN Eletrónico: 978-3-540-74976-9

Editora: Springer

Indexação

ISI Web of Knowledge - 46 Citações

Scopus - 49 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

Outras Informações

ID Authenticus: P-004-DTG

DOI: 10.1007/978-3-540-74976-9_63

Abstract (EN): Cost-sensitive learning is a key technique for addressing many real world data mining applications. Most existing research has been focused on classification problems. In this paper we propose a framework for evaluating regression models in applications with non-uniform costs and benefits across the domain of the continuous target variable. Namely, we describe two metrics for asserting the costs and benefits of the predictions of any model given a set of test cases. We illustrate the use of our metrics in the context of a specific type of applications where non-uniform costs are required: the prediction of rare extreme values of a continuous target variable. Our experiments provide clear evidence of the utility of the proposed framework for evaluating the merits of any model in this class of regression domains.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Notas: Part of the Lecture Notes in Computer Science book series (LNCS, volume 4702) Print ISBN: 978-3-540-74975-2

Nº de páginas: 8