Discovery of functional relationships in multi-relational data using inductive logic programming

• 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: FOURTH IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS

Páginas: 319-322

4th IEEE International Conference on Data Mining

Brighton, ENGLAND, NOV 01-04, 2004

Indexação

ISI Proceedings

ISI Web of Knowledge - 1 Citação

Scopus - 1 Citação

INSPEC

Classificação Científica

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

CORDIS: Ciências Físicas > Ciência de computadores > Programação

Outras Informações

ID Authenticus: P-000-CRN

DOI: 10.1109/icdm.2004.10053

Abstract (EN): ILP systems have been largely applied to datamining classification tasks with a considerable success. The use of ILP systems in regression tasks has been far less successful. Current systems have very limited numerical reasoning capabilities, which limits the application of ILP to discovery of functional relationships of numeric nature. This paper proposes improvements in numerical reasoning capabilities of ILP systems for dealing with regression tasks. 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 PAC method to evaluate learning performance. We have found these extensions essential to improve on results over machine learning and statistical-based algorithms used in the empirical evaluation study.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 4