Bagging different instead of similar models for regression and classification problems. (18th December 2009)

• Record Type: Journal Article
• Authors: Kotsiantis, Sotiris B.; Kanellopoulos, Dimitris N.
• Is Part Of: International journal of computer applications technology. Volume 37:Number 1(2010)
• Page Start: 20
• Page End: 28
• Publication Date: 2009-12-18
• Subjects: classifiers -- machine learning -- data mining -- regressors -- classification -- regression -- voting -- averaging -- learner ensembles; Technology -- Data processing -- Periodicals; 620.00285
• Languages: English
• ISSNs: 0952-8091

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.