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

• Type: Article in International Conference Proceedings Book
• Date: 2004

Authors

Alexessander Alves (Author) Other; Rui Camacho (Author) FEUP; Eugénio Oliveira (Author) FEUP

Conference proceedings International

Title: 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

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

CORDIS: Physical sciences > Computer science > Programming

Other information

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.

Language: English

Type (Professor's evaluation): Scientific