Using Bagging and Cross-Validation to improve ensembles based on penalty terms

Abstract

Decorrelated and CELS are two ensembles that modify the learning procedure to increase the diversity among the networks of the ensemble. Although they provide good performance according to previous comparatives, they are not as well known as other alternatives, such as Bagging and Boosting, which modify the learning set in order to obtain classifiers with high performance. In this paper, two different procedures are introduced to Decorrelated and CELS in order to modify the learning set of each individual network and improve their accuracy. The results show that these two ensembles are improved by using the two proposed methodologies as specific set generators. © 2011 Springer-Verlag.