Designing Fusers on the Basis of Discriminants – Evolutionary and Neural Methods of Training
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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.
Keywordscombining classifiers evolutionary algorithms neural networks fuser design
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