Improving numerical reasoning capabilities of inductive logic programming systems

• Type: Article in International Scientific Journal
• Date: 2004

Authors

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

Journal

Title: Lecture Notes in Computer Science

Vol. 10

Pages: 195-204

ISSN: 0302-9743

Publisher: Springer Nature

Indexing

ISI Proceedings

ISI Web of Knowledge - 0 Citations

ISI Web of Science

Scopus - 1 Citation

COMPENDEX

INSPEC

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10