Abstract
The paper presents novel algorithm of decision making in multiple classifier system (MCS), which response is based on weighted fusion of discriminating functions derived from a pool of elementary classifiers. Radial basis function model are used to establish the weights of the classifiers over a feature space. For best exploitation of knowledge collected by the classifiers parameters of the weight functions are set during learning process of the MCS that aims at minimizing misclassification rate of the MCS. Quality of the proposed radial basis function MCS (RB MCS) is verified in the set of experiments carried out on the set of benchmark datasets derived from UCI repository.
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Jackowski, K. (2010). Multiple Classifier System with Radial Basis Weight Function. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds) Hybrid Artificial Intelligence Systems. HAIS 2010. Lecture Notes in Computer Science(), vol 6076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13769-3_66
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DOI: https://doi.org/10.1007/978-3-642-13769-3_66
Publisher Name: Springer, Berlin, Heidelberg
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