E-CIDIM: Ensemble of CIDIM classifiers

Abstract

An active research area in Machine Learning is the construction of multiple classifier systems to increase learning accuracy of simple classifiers. In this paper we present E-CIDIM, a multiple classifier system designed to improve the performance of CIDIM, an algorithm that induces small and accurate decision trees. E-CIDIM keeps a maximum number of trees and it induces new trees that may substitute the old trees in the ensemble. The substitution process finishes when none of the new trees improves the accuracy of any of the trees in the ensemble after a pre-configured number of attempts. In this way, the accuracy obtained thanks to an unique instance of CIDIM can be improved. In reference to the accuracy of the generated ensembles, E-CIDIM competes well against bagging and boosting at statistically significance confidence levels and it usually outperforms them in the accuracy and the average size of the trees in the ensemble. © Springer-Verlag Berlin Heidelberg 2005.