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Complementarity among classifiers is a crucial aspect in classifier combination. A combined classifier is significantly superior to the individual classifiers only if they strongly complement each other. In this paper a complementarity-based analysis of sets of classifier is proposed for investigating the behaviour of multi-classifier systems, as new classifiers are added to the set. The experimental...
This paper presents a framework for the analysis of similarity among abstract-level classifiers and proposes a methodology for the evaluation of combination methods. In this paper, each abstract-level classifier is considered as a random variable, and sets of classifiers with different degrees of similarity are systematically simulated, combined, and studied. It is shown to what extent the performance...
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