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Improving Product by Moderating k-NN Classifiers

Improving Product by Moderating k-NN Classifiers

Book SeriesLecture Notes in Computer Science
PublisherSpringer Berlin / Heidelberg
ISSN0302-9743 (Print) 1611-3349 (Online)
VolumeVolume 2096/2001
BookMultiple Classifier Systems
DOI10.1007/3-540-48219-9
Copyright2001
ISBN978-3-540-42284-6
DOI10.1007/3-540-48219-9_43
Pages429-439
Subject CollectionComputer Science
SpringerLink DateMonday, January 01, 2001
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Improving Product by Moderating k-NN Classifiers

F. M. Alkoot6 Contact Information and J. Kittler6 Contact Information

(6)  Centre for Vision, Speech and Signal Processing, School of Electronics, Computing and Mathematics, University of Surrey, Guildford, GU2 5XH, UK
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.

Contact Information F. M. Alkoot
Email: F.Alkoot@eim.surrey.ac.uk

Contact Information J. Kittler
Email: J.Kittler@eim.surrey.ac.uk
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