Plurality voting-based multiple classifier systems: statistically independent with respect to dependent classifier sets

DOI10.1016/S0031-3203(01)00227-8zbMath1006.68926MaRDI QIDQ1860211

Mübeccel Demirekler, Hakan Altınçay

Publication date: 20 February 2003

Published in: Pattern Recognition (Search for Journal in Brave)

zbMATH Keywords

Bayesian formalismmultiple classifier systemsstatistical classifier combinationstatistical pattern recognitionclassifier selectionplurality votingadding new classifiersbest distributionsindependent distributionsworst distributions

Mathematics Subject Classification ID

Pattern recognition, speech recognition (68T10) Computing methodologies and applications (68U99)

Related Items (2)

On the independence requirement in Dempster-Shafer theory for combining classifiers providing statistical evidence ⋮ On the independence requirement in Dempster-Shafer theory for combining classifiers providing statistical evidence

Cites Work

• A method of combining multiple probabilistic classifiers through soft competition on different feature sets
• An approach to nonlinear programming
• A statistical unified framework for rank-based multiple classifier decision combination

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