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Particle swarm optimisation assisted classification using elastic net prefiltering

Hong, X., Gao, J., Chen, S. and Harris, C. J. (2013) Particle swarm optimisation assisted classification using elastic net prefiltering. Neurocomputing, 122. pp. 210-220. ISSN 0925-2312

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To link to this item DOI: 10.1016/j.neucom.2013.06.030

Abstract/Summary

A novel two-stage construction algorithm for linear-in-the-parameters classifier 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.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:34103
Uncontrolled Keywords:Classification; Bayesian evidence; Elastic net; Forward regression; Regularisation; Particle swarm optimisation
Publisher:Elsevier

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