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In this paper we propose a simple and effective combined classifier based on the data reduction carried-out through applying fuzzy C-means clustering and differential evolution techniques. The idea is to produce clusters from the training set instances applying fuzzy C-means algorithm. In further step cluster centroids are used as seeds in the differential evolution algorithm to construct prototypes, each representing a single cluster. Simple distance-based weak classifiers are then used to produce the Ada Boost combined classifier. The approach has been validated experimentally. Computational experiment results confirm good quality of the proposed classifier.
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