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Soft Computing
Erschienen in: Soft Computing 7/2011

01.07.2011 | Original Paper

REGAL-TC: a distributed genetic algorithm for concept learning based on REGAL and the treatment of counterexamples

verfasst von: L. Ignacio Lopez, Juan M. Bardallo, Miguel A. De Vega, Antonio Peregrin

Erschienen in: Soft Computing | Ausgabe 7/2011

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Abstract

This paper presents a proposal to improve REGAL, a concept learning system based on a distributed genetic algorithm that learns first-order logic multi-modal concept descriptions in the field of classification tasks. This algorithm has been a pioneer system and source of inspiration for others. Studying the philosophy and experimental behaviour of REGAL, we propose some improvements based principally on a new treatment of counterexamples that promote its underlying goodness in order to achieve better performances in accuracy, interpretability and scalability, so that the new system meets the main requirements for classification rules extraction in data mining. The experimental study carried out shows valuable improvements compared with both REGAL and G-Net distributed genetic algorithms and interesting results compared with some state-of-the-art representative algorithms in this field.
Vorheriger Artikel Effect algebras are conditionally residuated structures
Nächster Artikel A memetic ant colony optimization algorithm for the dynamic travelling salesman problem

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Metadaten
Titel
REGAL-TC: a distributed genetic algorithm for concept learning based on REGAL and the treatment of counterexamples
verfasst von
L. Ignacio Lopez
Juan M. Bardallo
Miguel A. De Vega
Antonio Peregrin
Publikationsdatum
01.07.2011
Verlag
Springer-Verlag
Erschienen in
Soft Computing / Ausgabe 7/2011
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-010-0678-8

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