Multiple Classifier Fusion Method based on Local Competence

E. Kim and Y. Lee (Korea)


Multiple classifiers, Combination, Local Competence, Machine Learning..


The diversity of application domains makes it difficult to find a highly reliable classification algorithm for sufficiently interesting tasks. Recently, classifier combination to overcome these drawbacks has aroused great interest to the pattern recognition community and various combination schemes have been devised. In this paper we propose a new combining method, which harness the local competence of each classifier in the combining process. This method learns the local competence of each classifier using training data and if an unknown data is given, the learned knowledge is used to evaluate the outputs of individual classifiers. An empirical evaluation using five real data sets has shown that this method achieves a promising performance and outperforms the best single classifiers and other known combining methods we tried.

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