Abstract
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.
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Bonissone, P., Goebel, K., Yan, W. (2004). Classifier Fusion Using Triangular Norms. In: Roli, F., Kittler, J., Windeatt, T. (eds) Multiple Classifier Systems. MCS 2004. Lecture Notes in Computer Science, vol 3077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25966-4_15
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DOI: https://doi.org/10.1007/978-3-540-25966-4_15
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