Combination of Multiple Classifiers Using Local Accuracy Estimates

Authors:
Kevin Woods;W. Philip Kegelmeyer, Jr.;Kevin Bowyer
Affiliations:
-;-;-
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1997

Citing 3
Cited 167

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Abstract

This paper presents a method for combining classifiers that uses estimates of each individual classifier's local accuracy in small regions of feature space surrounding an unknown test sample. An empirical evaluation using five real data sets confirms the validity of our approach compared to some other Combination of Multiple Classifiers algorithms. We also suggest a methodology for determining the best mix of individual classifiers.