Multiple expert classification: a new methodology for parallel decision fusion |
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Authors: | AFR Rahman MC Fairhurst |
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Affiliation: | (1) Electronic Engineering Laboratory, University of Kent, Canterbury, Kent CT2 7NT, UK , GB |
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Abstract: | Abstract. A new parallel hybrid decision fusion methodology is proposed. It is demonstrated that existing parallel multiple expert
decision combination approaches can be divided into two broad categories based on the implicit decision emphasis implemented.
The first category consists of methods implementing computationally intensive decision frameworks incorporating a priori information
about the target task domain and the reliability of the participating experts, while the second category encompasses approaches
implementing group consensus without assigning any importance to the reliability of the experts and ignoring other contextual
information. The methodology proposed in this paper is a hybridisation of these two approaches and has shown significant performance
enhancements in terms of higher overall recognition rates along with lower substitution rates. Detailed analysis using two
different databases supports this claim.
Received January 19, 1999 / Revised March 20, 2000 |
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Keywords: | : Multiple expert configurations – Decision fusion – Hybrid framework – Character recognition |
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