A random subspace method that uses different instead of similar models for regression and classification problems

S.B. Kotsiantis

International Journal of Information and Decision Sciences, 2011, vol. 3, issue 2, 173-188

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. (search for similar items in EconPapers)
Date: 2011
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