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Particle swarm optimisation assisted classification using elastic net prefiltering

Particle swarm optimisation assisted classification using elastic net prefiltering
Particle swarm optimisation assisted classification using elastic net prefiltering
A novel two-stage construction algorithm for linear-in-the-parameters classifiers is proposed, aiming at noisy two-class classification problems. The purpose of the first stage is to produce a prefiltered signal that is used as the desired output for the second stage to construct a sparse linear-in-the-parameters classifier. For the first stage learning of generating the prefiltered signal, a two-level algorithm is introduced to maximise the model's generalisation capability, in which an elastic net model identification algorithm using singular value decomposition is employed at the lower level while the two regularisation parameters are selected by maximising the Bayesian evidence using a particle swarm optimization algorithm. Analysis is provided to demonstrate how "Occam's razor" is embodied in this approach. The second stage of sparse classifier construction is based on an orthogonal forward regression with the D-optimality algorithm. Extensive experimental results demonstrate that the proposed approach is effective and yields competitive results for noisy data sets
0925-2312
210-220
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Gao, Junbin
a3dcab84-9675-402c-a19e-d41ea9973f3a
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Harris, Chris J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Gao, Junbin
a3dcab84-9675-402c-a19e-d41ea9973f3a
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Harris, Chris J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a

Hong, Xia, Gao, Junbin, Chen, Sheng and Harris, Chris J. (2013) Particle swarm optimisation assisted classification using elastic net prefiltering. Neurocomputing, 122, 210-220. (doi:10.1016/j.neucom.2013.06.030).

Record type: Article

Abstract

A novel two-stage construction algorithm for linear-in-the-parameters classifiers is proposed, aiming at noisy two-class classification problems. The purpose of the first stage is to produce a prefiltered signal that is used as the desired output for the second stage to construct a sparse linear-in-the-parameters classifier. For the first stage learning of generating the prefiltered signal, a two-level algorithm is introduced to maximise the model's generalisation capability, in which an elastic net model identification algorithm using singular value decomposition is employed at the lower level while the two regularisation parameters are selected by maximising the Bayesian evidence using a particle swarm optimization algorithm. Analysis is provided to demonstrate how "Occam's razor" is embodied in this approach. The second stage of sparse classifier construction is based on an orthogonal forward regression with the D-optimality algorithm. Extensive experimental results demonstrate that the proposed approach is effective and yields competitive results for noisy data sets

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Published date: 25 December 2013
Organisations: Southampton Wireless Group

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Local EPrints ID: 356925
URI: http://eprints.soton.ac.uk/id/eprint/356925
ISSN: 0925-2312
PURE UUID: 6d10274e-9a10-45de-9a8f-019299d94123

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Date deposited: 03 Oct 2013 10:10
Last modified: 06 Oct 2020 23:26

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Contributors

Author: Xia Hong
Author: Junbin Gao
Author: Sheng Chen
Author: Chris J. Harris

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