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2004
Springer

Classifier Fusion Using Triangular Norms

9 years 3 months ago
Classifier Fusion Using Triangular Norms
This paper describes a method for fusing a collection of classifiers where the fusion can compensate for some positive correlation among the classifiers. Specifically, it does not require the assumption of evidential independence of the classifiers to be fused (such as Dempster Shafer’s fusion rule). The proposed method is associative, which allows fusing three or more classifiers irrespective of the order. The fusion is accomplished using a generalized intersection operator (T-norm) that better represents the possible correlation between the classifiers. In addition, a confidence measure is produced that takes advantage of the consensus and conflict between classifiers.
Piero P. Bonissone, Kai Goebel, Weizhong Yan
Added 02 Jul 2010
Updated 02 Jul 2010
Type Conference
Year 2004
Where MCS
Authors Piero P. Bonissone, Kai Goebel, Weizhong Yan
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