Evolving Classifier Ensemble With Gene Expression Programming

Gene expression programming (GEP) is a kind of geno-type/phenotype based evolutionary computation(EC) algorithm. GEP has been successfully applied in data mining (DM) fields such as regression, classification and association rules mining. Although GEP has been used as a raw DM tool in these fields, its potential to combine with DM techniques has not been well studied in both DM and EC fields. In this paper, two ensemble methods, namely bagging and boosting, together with other DM tools available in Weka platform, are applied to improve the learning ability of GEP classifiers. Results show that the two popular ensemble methods can improve classification accuracy of raw GEP classifiers. What´s more, bagging outperforms boosting in GEP classifier learning.