Abstract
Combined classifiers can show better performance than the best single classifier used in isolation, while involving little additional computational effort. This is because different classifier can potentially offer complementary information about the pattern and group decisions can take the advantage of the benefit of combining multiple classifiers in making final decision. In this paper we propose a new combining method, which harness the local confidence of each classifier in the combining process. This method learns the local confidence 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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© 2002 Springer-Verlag Berlin Heidelberg
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Kim, E., Kim, W., Lee, Y. (2002). Classifier Fusion Using Local Confidence. In: Hacid, MS., Raś, Z.W., Zighed, D.A., Kodratoff, Y. (eds) Foundations of Intelligent Systems. ISMIS 2002. Lecture Notes in Computer Science(), vol 2366. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48050-1_62
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DOI: https://doi.org/10.1007/3-540-48050-1_62
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