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

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  • S.B. Kotsiantis

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.

Suggested Citation

  • S.B. Kotsiantis, 2011. "A random subspace method that uses different instead of similar models for regression and classification problems," International Journal of Information and Decision Sciences, Inderscience Enterprises Ltd, vol. 3(2), pages 173-188.
  • Handle: RePEc:ids:ijidsc:v:3:y:2011:i:2:p:173-188
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