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

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, performed on 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.