Committees of Adaboost Ensembles with Modified Emphasis Functions
Articles
Overview
published in
- NEUROCOMPUTING Journal
publication date
- March 2010
start page
- 1289
end page
- 1292
issue
- 7-9
volume
- 73
Digital Object Identifier (DOI)
International Standard Serial Number (ISSN)
- 0925-2312
Electronic International Standard Serial Number (EISSN)
- 1872-8286
abstract
- Real Adaboost ensembles with weighted emphasis (RA-we) on erroneous and critical (near the classification boundary) samples have recently been proposed, leading to improved performance when an adequate combination of these terms is selected. However, finding the optimal emphasis adjustment is not an easy task. In this paper, we propose to make a fusion of the outputs of RA-we ensembles trained with different emphasis adjustments by means of a generalized voting scheme. The resulting committee of RA-we ensembles can retain the performance of the best RA-we component and even, occasionally, can improve it. Additionally, we present an ensemble selection strategy that removes from the committee RA-we ensembles with very poor performance. Experimental results show that these committees frequently outperform RA and RA-we with cross validated emphasis.