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