Enhancing consensus in multiple expert decision fusion

Fairhurst, Michael and Rahman, Ahmad Fuad Rezaur (2000) Enhancing consensus in multiple expert decision fusion. IEE Proceedings: Vision Image and Signal Processing, 147 (1). pp. 39-46. ISSN 1350-245X. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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ENCORE (ENhanced COnsensus in REcognition) is a new classifier structure based on decision fusion of multiple experts (classifiers). When more than one classifier (expert) is available and it is required to combine their decisions, a fundamental aim may be to incorporate a sense of decision consensus. Alternatively, it may be considered important to ensure that appropriate weights are given to more competent classifiers. These two requirements may be mutually contradictory, as the first aims to ensure giving higher emphasis to the best decision delivered by the majority, while the second aims to ensure finding the most appropriate classifier and then giving higher weight to its decision. A new multiple expert classifier (ENCORE) is introduced which implements a decision consensus approach, but the quality of the consensus is evaluated in terms of the past track record of the consenting experts before it is accepted. The ENCORE system has been found to offer greater flexibility of performance in a character recognition task. Detailed analysis using two different databases illustrates the capabilities of this system, although the structure proposed is generic in nature, and may be readily applied to other task domains.

Item Type: Article
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculties > Science Technology and Medical Studies > School of Engineering and Digital Arts
Depositing User: O.O. Odanye
Date Deposited: 18 May 2009 23:49
Last Modified: 17 Jul 2014 08:14
Resource URI: https://kar.kent.ac.uk/id/eprint/16084 (The current URI for this page, for reference purposes)
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