Committees of Adaboost Ensembles with Modified Emphasis Functions Articles uri icon

publication date

  • March 2010

start page

  • 1289

end page

  • 1292

issue

  • 7-9

volume

  • 73

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.