PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Committees of Adaboost Ensembles with modified emphasis functions
Vanessa Gómez-Verdejo, Jerónimo Arenas-Garcia and Aníbal R. Figueiras-Vidal
Neurocomputing 2009.

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.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:6690
Deposited By:Jerónimo Arenas-Garcia
Deposited On:08 March 2010