Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

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

A random subspace method that uses different instead of similar models for regression and... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Information and Decision Sciences Inderscience Publishers

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

Loading next page...
 
/lp/inderscience-publishers/a-random-subspace-method-that-uses-different-instead-of-similar-models-jSgdufkbp5
Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1756-7017
eISSN
1756-7025
DOI
10.1504/IJIDS.2011.040422
Publisher site
See Article on Publisher Site

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.

Journal

International Journal of Information and Decision SciencesInderscience Publishers

Published: Jan 1, 2011

There are no references for this article.