title: A dynamically adjusted mixed emphasis method for building boosting ensembles.
abstract: 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.
authors.affiliation: Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Spain. vanessa@tsc.uc3m.es
authors.forename: Vanessa
authors.initials: V
authors.lastname: Gomez-Verdejo
authors.forename: Jerónimo
authors.initials: J
authors.lastname: Arenas-Garcia
authors.forename: Aníbal R
authors.initials: AR
authors.lastname: Figueiras-Vidal
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007858:Learning / Q000502:physiology*; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D025341:Principal Component Analysis