PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Dynamically Adjusted Mixed Emphasis Method for Building Boosting Ensembles
Vanessa Gómez-Verdejo, Jerónimo Arenas-Garcia and Aníbal R. Figueiras-Vidal
IEEE Transactions on Neural Networks Volume 19, pp. 3-17, 2008.


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.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:5017
Deposited By:Jerónimo Arenas-Garcia
Deposited On:24 March 2009