Weighting Individual Classifiers by Local Within-Class Accuracies

Authors:
Shiliang Sun
Affiliations:
Department of Computer Science and Technology, East China Normal University, Shanghai, China 200241
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Year:
2009

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Abstract

A new method for weighting individual classifiers in a multiple classifier system based on their local within-class accuracies is proposed. For an example to be classified distance metric learning is applied to determine the within-class nearest neighbors. Then the local within-class accuracy of an individual classifier for classifying this example is judged by its performance on these neighbors, which is further used to weight the individual classifier. Experimental results on nine data sets show the effectiveness of the proposed weighting method.