Neural Computing and Applications

, Volume 18, Issue 2, pp 105–108 | Cite as

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

Original Article

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.

Keywords

Particle swarm optimization Evidential k-NN classifier Random subspace 

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Copyright information

© Springer-Verlag London Limited 2007

Authors and Affiliations

  1. 1.DEIS, IEIIT-CNR, Università di BolognaBolognaItaly

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