A Dynamically Adjusted Mixed Emphasis Method for Building Boosting Ensembles
Articles
Overview
published in
publication date
- January 2008
start page
- 3
end page
- 17
issue
- 1
volume
- 19
Digital Object Identifier (DOI)
International Standard Serial Number (ISSN)
- 2162-237X
Electronic International Standard Serial Number (EISSN)
- 2162-2388
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.