Model validation: A statistical-based criterium of hypotheses acceptance in numerical reasoning

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2004

Autores

Alexessander Alves (Autor) Outra; Rui Camacho (Autor) FEUP; Eugénio Oliveira (Autor) FEUP

Ata de Conferência Internacional

Título: 14th International Conference on Inductive Logic Programming (ILP 2004)

14th International Conference on Inductive Logic Programming (ILP 2004)

Porto, Portugal, 6 a 8 de Setembro de 2004

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Engenharia electrotécnica, electrónica e informática

CORDIS: Ciências Físicas > Ciência de computadores > Programação

Outras Informações

Abstract (EN): Current ILP systems that perform numerical reasoning, select the best hypothesis using exclusively the scored value of the cost function. The cost function, by itself, cannot guarantee the goodness-offit of the induced hypotheses in numerical domains. Consequently the induced theory may not capture the overall structure of the underlying process that generated data. This paper proposes a statistical-based criterion for hypotheses acceptance, called model validation, that assess the goodness-of-fit of the induced hypotheses in numerical domains. We have found this extension essential to improve on results over ML and statistical-based algorithms used in the empirical evaluation study.

Idioma: Inglês

Tipo (Avaliação Docente): Científica