Towards Utility Maximization in Regression

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2012

Autores

ribeiro, rp (Autor) FCUP

Ata de Conferência Internacional

Título: 12TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2012)

Páginas: 179-186

12th IEEE International Conference on Data Mining (ICDM)

Brussels, BELGIUM, DEC 10-13, 2012

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 1 Citação

Classificação Científica

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

Outras Informações

ID Authenticus: P-005-KEW

DOI: 10.1109/icdmw.2012.82

Abstract (EN): Utilitybased learning is a key technique for addressing many real world data mining applications, where the costs/benefits are not uniform across the domain of the target variable. Still, most of the existing research has been focused on classification problems. In this paper we address a related problem. There are many relevant domains (e. g. ecological, meteorological, finance) where decisions are based on the forecast of a numeric quantity (i.e. the result of a regression model). The goal of the work on this paper is to present an evaluation framework for applications where the numeric outcome of a regression model may lead to different costs/benefits as a consequence of the actions it entails. The new metric provides a more informed estimate of the utility of any regression model, given the application-specific preference biases, and hence makes more reliable the comparison and selection between alternative regression models. We illustrate the objective of our evaluation methodology on a real-life application and also carry a set of experiments over a subset of our target regression tasks: the prediction of rare and extreme values. Results show the effectiveness of our proposed utility metric for identifying the models that perform better on this type of applications.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 8