Evolving Sets of Symbolic Classifiers into a Single Symbolic Classifier Using Genetic Algorithms
Flávia Bernardini, Ronaldo Cristiano Prati, Maria Carolina Monard · 2008
For a given data set, different learning algorithms typically provide different classifiers. Although it is possible to simply select the most successful classifier, the less successful classifiers could have potentially valuable information that may be wasted. This work proposes GAESC, an algorithm for evolving a set of classifiers into a single symbolic classifier using genetic algorithms. Individuals are formed by rules collected from symbolic classifiers and rules from association classification rules. Experimental results in three data sets from UCI show that GAESC outperforms the single symbolic classifiers in terms of classification error rate.