Boosting Classifiers Regionally

This paper presents a newalgorithm for Boosting the performance of an ensembleof classifiers. In Boosting, a series of classifiers is usedto predict the class of data wherelater members of the series concentrate on training data that is incorrectly predicted by earlier members. To makea prediction abouta newpattern, each classifier predicts the class of the pattern and these predictions are then combined. In standard Boosting, the predictions are combined by weightingthe predictions bya termrelated to the accuracyof the classifier on the training data. This approachignores the fact that later classifiers focuson smallsubsets of the patterns and thus mayonly be good at classifying similar patterns. In RegionBoost, this problemis addressed by weighting each classifier’s predictions by a factor measuringhowwell that classifier performson similar patterns. In this paper weexamineseveral methodsfor determininghowwell a classifier performson similar patterns. Empirical tests indicate RegionBoost producesgains in performancefor somedata sets andhas little effect onothers.

Authors

• Richard Maclin Edward Wild

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