Improving Product by Moderating k-NN Classifiers

Authors:
Fuad M. Alkoot;Josef Kittler
Affiliations:
-;-
Venue:
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Year:
2001

Citing 8
Cited 1

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Abstract

The veto effect caused by contradicting experts outputting zero probability estimates leads to fusion strategies performing sub optimally. This can be resolved using Moderation. The Moderation formula is derived for the k-NN classifier using a bayesian prior. The merits of moderation are examined on real data sets.