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Use of Dempster-Shafer theory to combine classifiers which use different class boundaries. (English) Zbl 1035.68099

Summary: In this paper we present the Dempster-Shafer theory as a framework within which the results of a Bayesian network classifier and a fuzzy logic-based classifier are combined to produce a better final classification. We deal with the case when the two original classifiers use different classes for the outcome. The problem of different classes is solved by using a superset of finer classes which can be combined to produce classes according to either of the two classifiers. Within the Dempster-Shafer formalism not only can the problem of different number of classes be solved, but the relative reliability of the classifiers can also be considered.

MSC:

68T10 Pattern recognition, speech recognition
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