XCS and GALE: A Comparative Study of Two Learning Classifier Systems on Data Mining

Authors:
Ester Bernadó i Mansilla;Xavier Llorà;Josep Maria Garrell i Guiu
Affiliations:
-;-;-
Venue:
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
Year:
2001

Citing 25
Cited 40

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Abstract

This paper compares the learning performance, in terms of prediction accuracy, of two genetic-based learning systems, XCS and GALE, with six well-known learning algorithms, coming from instance based learning, decision tree induction, rule-learning, statistical modeling and support vector machines. The experiments, performedon several datasets, show the suitability of the genetic-based learning classifier systems for classification tasks. Both XCS and GALE significantly achieved better results than IB1 and Naive Bayes. Besides, any method could not outperform XCS and GALE significantly.