Boosting classifiers regionally

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Scopus citations


This paper presents a new algorithm for Boosting the performance of an ensemble of classifiers. In Boosting, a series of classifiers is used to predict the class of data where later members of the series concentrate on training data that is incorrectly predicted by earlier members. To make a prediction about a new pattern, each classifier predicts the class of the pattern and these predictions are then combined. In standard Boosting, the predictions are combined by weighting the predictions by a term related to the accuracy of the classifier on the training data. This approach ignores the fact that later classifiers focus on small subsets of the patterns and thus may only be good at classifying similar patterns. In RegionBoost, this problem is addressed by weighting each classifier's predictions by a factor measuring how well that classifier performs on similar patterns. In this paper we examine several methods for determining how well a classifier performs on similar patterns. Empirical tests indicate RegionBoost produces gains in performance for some data sets and has little effect on others.

Original languageEnglish (US)
Title of host publicationProceedings of the National Conference on Artificial Intelligence
Editors Anon
Number of pages6
StatePublished - Jan 1 1998
EventProceedings of the 1998 15th National Conference on Artificial Intelligence, AAAI - Madison, WI, USA
Duration: Jul 26 1998Jul 30 1998


OtherProceedings of the 1998 15th National Conference on Artificial Intelligence, AAAI
CityMadison, WI, USA


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