Particle swarm optimization for ensembling generation for evidential k-nearest-neighbour classifier |
| |
Authors: | Loris Nanni Alessandra Lumini |
| |
Affiliation: | (1) DEIS, IEIIT-CNR, Università di Bologna, Viale Risorgimento 2, 40136 Bologna, Italy |
| |
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 |
本文献已被 SpringerLink 等数据库收录! |