Authors: Sotiris B. Kotsiantis, Dimitris N. Kanellopoulos
Addresses: Educational Software Development Laboratory, Department of Mathematics, University of Patras, University Campus, 26504, Rio, Patras, Greece. ' Educational Software Development Laboratory, Department of Mathematics, University of Patras, University Campus, 26504, Rio, Patras, Greece
Abstract: Even though many ensemble techniques have been proposed, there is as yet no clear picture of which method is best. In this study, we propose a technique that uses different subsets of the same training dataset with the concurrent usage of a voting (for classification problems) or averaging methodology (for regression problems) for combining different learners instead of similar learners. We performed a comparison of the proposed ensemble with other well known ensembles that use the same base learners and the proposed technique had better accuracy in most cases.
Keywords: classifiers; machine learning; data mining; regressors; classification; regression; voting; averaging; learner ensembles.
International Journal of Computer Applications in Technology, 2010 Vol.37 No.1, pp.20 - 28
Published online: 17 Dec 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article