An Application of OWA Operators to the Aggregation of Multiple Classification Decisions

Ludmila I. Kuncheva ()
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Ludmila I. Kuncheva: Imperial College, Department of Electrical and Electronic Engineering

A chapter in The Ordered Weighted Averaging Operators, 1997, pp 330-343 from Springer

Abstract: Abstract The paper considers a classification scheme made up by pooling together multiple classifiers and aggregating their decisions. The individual decisions are treated as degrees of membership assigned by the classifier to the object to be classified. We are interested in how the OWA operators compare to simple voting, linear and logarithmic techniques. In general, all the aggregation schemes appear to be of the same quality, superior to the single classifiers. It was found that OWA operators tend to generalize better than their competitors when the individual classifiers are overtrained. The idea is illustrated on a real and on an artificial data set.

Keywords: Pattern recognition; aggregation of multiple classifiers; committees of networks (search for similar items in EconPapers)
Date: 1997
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4615-6123-1_25

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DOI: 10.1007/978-1-4615-6123-1_25

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