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

Chen, S., Hong, X. and Harris, C. J. (2010) Radial basis function classifier construction using particle swarm optimisation aided orthogonal forward regression. In: The 2010 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 3418-3423. ISBN 9781424469161

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To link to this item DOI: 10.1109/IJCNN.2010.5596855

Abstract/Summary

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 fixednode 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.

Item Type:Book or Report Section
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:16728
Additional Information:The conference was held in Barcelona, Spain, 18-23 July 2010.
Publisher:IEEE

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