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Proceedings Paper

Using correlation-based measures to select classifiers for decision fusion
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Paper Abstract

This paper explores classifier fusion problems where the task is selecting a subset of classifiers from a larger set with the goal to achieve optimal performance. To aid in the selection process we propose the use of several correlationbased diversity measures. We define measures that capture the correlation for n classifiers as opposed to pairs of classifiers only. We then suggest a sequence of steps in selecting classifiers. This method avoids the exhaustive evaluation of all classifier combinations which can become very large for larger sets of classifiers. We then report on observations made after applying that method to a data set from a real-world application. The classifier set chosen achieves close to optimal performance with a drastically reduced set of evaluation steps.

Paper Details

Date Published: 28 March 2005
PDF: 12 pages
Proc. SPIE 5813, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2005, (28 March 2005); doi: 10.1117/12.603981
Show Author Affiliations
Kai F. Goebel, GE Global Research Ctr. (United States)
Weizhong Yan, GE Global Research Ctr. (United States)


Published in SPIE Proceedings Vol. 5813:
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2005
Belur V. Dasarathy, Editor(s)

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