Improving numerical reasoning capabilities of inductive logic programming systems

• Tipo: Artigo em Revista Científica Internacional
• Data: 2004

Autores

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

Revista

Título: Lecture Notes in Computer Science

Vol. 10

Páginas: 195-204

ISSN: 0302-9743

Editora: Springer Nature

Indexação

ISI Proceedings

ISI Web of Knowledge - 0 Citações

ISI Web of Science

Scopus - 1 Citação

COMPENDEX

INSPEC

Classificação Científica

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

Outras Informações

ID Authenticus: P-000-C6K

Abstract (EN): Inductive Logic Programming (ILP) systems have been largely applied to classification problems with a considerable success. The use of ILP systems in problems requiring numerical reasoning capabilities has been far less successful. Current systems have very limited numerical reasoning capabilities, which limits the range of domains where the ILP paradigm may be applied. This paper proposes improvements in numerical reasoning capabilities of ILP systems. It proposes the use of statistical-based techniques like Model Validation and Model Selection to improve noise handling and it introduces a new search stopping criterium based on the PAG method to evaluate learning performance. We have found these extensions essential to improve on results mer statistical-based algorithms for time series forecasting used in the empirical evaluation study.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 10