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Radial basis function classifier construction using particle swarm optimisation aided orthogonal forward regression

Radial basis function classifier construction using particle swarm optimisation aided orthogonal forward regression
Radial basis function classifier construction using particle swarm optimisation aided orthogonal forward regression
We develop a particle swarm optimisation (PSO) aided orthogonal forward regression (OFR) approach for constructing radial basis function (RBF) classifiers with tunable nodes. At each stage of the OFR construction process, the centre vector and diagonal covariance matrix of one RBF node is determined efficiently by minimising the leave-one-out (LOO) misclassification rate (MR) using a PSO algorithm. Compared with the state-of-the-art regularisation assisted orthogonal least square algorithm based on the LOO MR for selecting fixed-node RBF classifiers, the proposed PSO aided OFR algorithm for constructing tunable-node RBF classifiers offers significant advantages in terms of better generalisation performance and smaller model size as well as imposes lower computational complexity in classifier construction process. Moreover, the proposed algorithm does not have any hyperparameter that requires costly tuning based on cross validation.
3418-3423
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Harris, Chris J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Harris, Chris J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a

Chen, Sheng, Hong, Xia and Harris, Chris J. (2010) Radial basis function classifier construction using particle swarm optimisation aided orthogonal forward regression. At IJCNN 2010 IJCNN 2010, Spain. 18 - 23 Jul 2010. pp. 3418-3423.

Record type: Conference or Workshop Item (Other)

Abstract

We develop a particle swarm optimisation (PSO) aided orthogonal forward regression (OFR) approach for constructing radial basis function (RBF) classifiers with tunable nodes. At each stage of the OFR construction process, the centre vector and diagonal covariance matrix of one RBF node is determined efficiently by minimising the leave-one-out (LOO) misclassification rate (MR) using a PSO algorithm. Compared with the state-of-the-art regularisation assisted orthogonal least square algorithm based on the LOO MR for selecting fixed-node RBF classifiers, the proposed PSO aided OFR algorithm for constructing tunable-node RBF classifiers offers significant advantages in terms of better generalisation performance and smaller model size as well as imposes lower computational complexity in classifier construction process. Moreover, the proposed algorithm does not have any hyperparameter that requires costly tuning based on cross validation.

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More information

Published date: July 2010
Additional Information: Event Dates: July 18-23, 2010
Venue - Dates: IJCNN 2010, Spain, 2010-07-18 - 2010-07-23
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 271406
URI: https://eprints.soton.ac.uk/id/eprint/271406
PURE UUID: e83536bb-cb26-4086-913c-f935c738e0fb

Catalogue record

Date deposited: 15 Jul 2010 14:42
Last modified: 18 Jul 2017 06:43

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Contributors

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

University divisions

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