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Combination of Multiple Classifiers Using Local Accuracy Estimates
April 1997 (vol. 19 no. 4)
pp. 405-410

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.

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Index Terms:
Combination of classifiers, dynamic classifier selection, local classifier accuracy, classifier fusion, ROC analysis.
Citation:
Kevin Woods, W. Philip Kegelmeyer Jr., Kevin Bowyer, "Combination of Multiple Classifiers Using Local Accuracy Estimates," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 4, pp. 405-410, April 1997, doi:10.1109/34.588027
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