Designing fusers on the basis of discriminants – evolutionary and neural methods of training

Authors:
Michal Wozniak;Marcin Zmyslony
Affiliations:
Department of Systems and Computer Networks, Wroclaw University of Technology, Wroclaw, Poland;Department of Systems and Computer Networks, Wroclaw University of Technology, Wroclaw, Poland
Venue:
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Year:
2010

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Abstract

The combining approach to classification is nowadays one of the most promising directions in pattern recognition There are many methods of decision-making that can be used by an ensemble of classifiers The most popular methods have their origins in voting, where the decision of a common classifier is a combination of individual classifiers' outputs, i.e class numbers or values of discriminants This work focuses on the problem of fuser design We propose to train a fusion block by algorithms that have their origin in neural and evolutionary approaches As we have shown in previous works, we can produce better combining classifiers than Oracle can Presented results of experiments confirm our previous observations.