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Proceedings Paper

Confusion-based fusion of classifiers
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Paper Abstract

Given a finite collection of classifiers trained on two-class data one wishes to fuse the classifiers to form a new classifier with improved performance. Typically, the fusion is done at the output level using logical ANDs and ORs. The proposed fusion is based on the location of the feature vector with respect to the expertise sets and confusion sets of the classifiers. Given a feature vector x, if any one of the classifiers is an expert on x then the fusion rule should be an OR. If the classifiers are confused at x then the fusion rule should be defined is such a way to reflect this confusion or uncertainty. We give this fusion rule that is based upon the confusion sets as well as the expertise sets. We believe that this fusion rule will produce classifiers that perform better than classifiers that resulted from other fusion rules.

Paper Details

Date Published: 11 March 2002
PDF: 9 pages
Proc. SPIE 4739, Applications and Science of Computational Intelligence V, (11 March 2002); doi: 10.1117/12.458704
Show Author Affiliations
Mark E. Oxley, Air Force Institute of Technology (United States)
Amy L. Magnus, Air Force Research Lab. (United States)

Published in SPIE Proceedings Vol. 4739:
Applications and Science of Computational Intelligence V
Kevin L. Priddy; Paul E. Keller; Peter J. Angeline, Editor(s)

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