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Paper deals with the problem of designing efficient classifiers for a special case of incremental concept drift. We focus on its classification based on the multiple classifier system. For the problem under consideration we propose four simple methods of combining classification and evaluate them via computer experiments.
Combining pattern recognition is the promising direction in designing an effective classifier systems. There are several approaches of collective decision-making, among them voting methods, where the decision is a combination of individual classifiers' outputs are quite popular. This article focuses on the problem of fuser design which uses continuous outputs of individual classifiers to make a decision...
Paper presents a brief survey of the main topics connected with Multiple Classifier Systems design task. On the beginning typical topologies are described and then we focus on ensemble and fuser design methods. For the last topic we show the limit of different approaches based on weighted voting.
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