Ahmadzadeh, M. R.; Petrou, M. Use of Dempster-Shafer theory to combine classifiers which use different class boundaries. (English) Zbl 1035.68099 PAA, Pattern Anal. Appl. 6, No. 1, 41-46 (2003). 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. Cited in 4 Documents MSC: 68T10 Pattern recognition, speech recognition Keywords:Bayesian networks; Classifier combination; Dempster-Shafer theory; Expert rules; Fuzzy logic PDFBibTeX XMLCite \textit{M. R. Ahmadzadeh} and \textit{M. Petrou}, PAA, Pattern Anal. Appl. 6, No. 1, 41--46 (2003; Zbl 1035.68099) Full Text: DOI