Predicting rare extreme values

• Tipo: Artigo em Revista Científica Internacional
• Data: 2006

Autores

Torgo, L (Autor) FEP; Ribeiro, R (Autor) FCUP

Revista

Título: Lecture Notes in Computer Science

Vol. 1

Páginas: 816-820

ISSN: 0302-9743

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 1 Citação

Classificação Científica

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

Outras Informações

ID Authenticus: P-004-P2Q

DOI: 10.1007/11731139_95

Abstract (EN): Modelling extreme data is very important in several application domains, like for instance finance, meteorology, ecology, etc.. This paper addresses the problem of predicting extreme values of a continuous variable. The main distinguishing feature of our target applications resides on the fact that these values are rare. Any prediction model is obtained by some sort of search process guided by a pre-specified evaluation criterion. In this work we argue against the use of standard criteria for evaluating regression models in the context of our target applications. We propose. a new predictive performance metric for this class of problems that our experiments show to perform better in distinguishing models that are more accurate at rare extreme values. This new evaluation metric could be used as the basis for developing better models in terms of rare extreme values prediction.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 5