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Neural Computing and Applications

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01.02.2009 | Original Article

Particle swarm optimization for ensembling generation for evidential k-nearest-neighbour classifier

verfasst von: Loris Nanni, Alessandra Lumini

Erschienen in: Neural Computing and Applications | Ausgabe 2/2009

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Abstract

The problem addressed in this paper concerns the ensembling generation for evidential k-nearest-neighbour classifier. An efficient method based on particle swarm optimization (PSO) is here proposed. We improve the performance of the evidential k-nearest-neighbour (EkNN) classifier using a random subspace based ensembling method. Given a set of random subspace EkNN classifier, a PSO is used for obtaining the best parameters of the set of evidential k-nearest-neighbour classifiers, finally these classifiers are combined by the “vote rule”. The performance improvement with respect to the state-of-the-art approaches is validated through experiments with several benchmark datasets.

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parameter of the EkNN classifier.

These algorithms are already implemented as MATLAB code by the authors of the algorithm and can be downloaded from http://www.hds.utc.fr/tdenoeux/software.htm.

It is implemented as in PSO MATLAB TOOLBOX; it is available at http://psotoolbox.sourceforge.net.

http://www.ics.uci.edu/mlearn/MLRepository.html.

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Titel: Particle swarm optimization for ensembling generation for evidential k-nearest-neighbour classifier
verfasst von: Loris Nanni
Alessandra Lumini
Publikationsdatum: 01.02.2009
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 2/2009
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-007-0162-2

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