A Dynamically Adjusted Mixed Emphasis Method for Building Boosting Ensembles Articles uri icon

publication date

  • January 2008

start page

  • 3

end page

  • 17


  • 1


  • 19

International Standard Serial Number (ISSN)

  • 2162-237X

Electronic International Standard Serial Number (EISSN)

  • 2162-2388


  • Progressively emphasizing samples that are difficult to classify correctly is the base for the recognized high performance of real Adaboost (RA) ensembles. The corresponding emphasis function can be written as a product of a factor that measures the quadratic error and a factor related to the proximity to the classification border; this fact opens the door to explore the potential advantages provided by using adjustable combined forms of these factors. In this paper, we introduce a principled procedure to select the combination parameter each time a new learner is added to the ensemble, just by maximizing the associated edge parameter, calling the resulting method the dynamically adapted weighted emphasis RA (DW-RA). A number of application examples illustrates the performance improvements obtained by DW-RA.