Title: A random subspace method that uses different instead of similar models for regression and classification problems
Authors: S.B. Kotsiantis
Addresses: Educational Software Development Laboratory, Department of Mathematics, University of Patras, University Campus, Rio, Patras, 26504, Greece
Abstract: Even though many ensemble techniques have been proposed, there is no clear picture of which method is best. In this study, we propose a technique that uses different subsets of the same feature set 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; random subspace; regression; classification; voting; averaging; feature sets; different learners; ensembles.
International Journal of Information and Decision Sciences, 2011 Vol.3 No.2, pp.173 - 188
Published online: 26 May 2011 *Full-text access for editors Access for subscribers Purchase this article Comment on this article