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A radial basis function network classifier to maximise leave-one-output mutual information

A radial basis function network classifier to maximise leave-one-output mutual information
A radial basis function network classifier to maximise leave-one-output mutual information
We develop an orthogonal forward selection (OFS) approach to construct radial basis function (RBF) network classifiers for two-class problems. Our approach integrates several concepts in probabilistic modelling, including cross validation, mutual information and Bayesian hyperparameter fitting. At each stage of the OFS procedure, one model term is selected by maximising the leave-one-out mutual information (LOOMI) between the classifier’s predicted class labels and the true class labels. We derive the formula of LOOMI within the OFS framework so that the LOOMI can be evaluated efficiently for model term selection. Furthermore, a Bayesian procedure of hyperparameter fitting is also integrated into the each stage of the OFS to infer the l2-norm based local regularisation parameter from the data. Since each forward stage is effectively fitting of a one-variable model, this task is very fast. The classifier construction procedure is automatically terminated without the need of using additional stopping criterion to yield very sparse RBF classifiers with excellent classification generalisation performance, which is particular useful for the noisy data sets with highly overlapping class distribution. A number of benchmark examples are employed to demonstrate the effectiveness of our proposed approach.
cross validation, mutual information, orthogonal forward selection, radial basis function classifier
1568-4946
9-18
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Qatawneh, Abdulrohman
b8c9c465-e9c6-43dc-b022-d291584f020d
Daqrouq, Khaled
1ba1973a-eec2-4290-9745-a3e61dc299d5
Sheikh, Muntasir
d336fc10-a1a6-4a86-ad81-75f07663c7c9
Morfeq, Ali
e70b81b2-1d0e-4de9-adae-0ee1098cc76e
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Qatawneh, Abdulrohman
b8c9c465-e9c6-43dc-b022-d291584f020d
Daqrouq, Khaled
1ba1973a-eec2-4290-9745-a3e61dc299d5
Sheikh, Muntasir
d336fc10-a1a6-4a86-ad81-75f07663c7c9
Morfeq, Ali
e70b81b2-1d0e-4de9-adae-0ee1098cc76e

Hong, Xia, Chen, Sheng, Qatawneh, Abdulrohman, Daqrouq, Khaled, Sheikh, Muntasir and Morfeq, Ali (2014) A radial basis function network classifier to maximise leave-one-output mutual information. Applied Soft Computing, 23, 9-18. (doi:10.1016/j.asoc.2014.06.003).

Record type: Article

Abstract

We develop an orthogonal forward selection (OFS) approach to construct radial basis function (RBF) network classifiers for two-class problems. Our approach integrates several concepts in probabilistic modelling, including cross validation, mutual information and Bayesian hyperparameter fitting. At each stage of the OFS procedure, one model term is selected by maximising the leave-one-out mutual information (LOOMI) between the classifier’s predicted class labels and the true class labels. We derive the formula of LOOMI within the OFS framework so that the LOOMI can be evaluated efficiently for model term selection. Furthermore, a Bayesian procedure of hyperparameter fitting is also integrated into the each stage of the OFS to infer the l2-norm based local regularisation parameter from the data. Since each forward stage is effectively fitting of a one-variable model, this task is very fast. The classifier construction procedure is automatically terminated without the need of using additional stopping criterion to yield very sparse RBF classifiers with excellent classification generalisation performance, which is particular useful for the noisy data sets with highly overlapping class distribution. A number of benchmark examples are employed to demonstrate the effectiveness of our proposed approach.

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Published date: October 2014
Keywords: cross validation, mutual information, orthogonal forward selection, radial basis function classifier
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 367051
URI: http://eprints.soton.ac.uk/id/eprint/367051
ISSN: 1568-4946
PURE UUID: 351a4e0e-b77e-4793-a112-d8fef5a825c1

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Date deposited: 22 Jul 2014 08:52
Last modified: 28 Oct 2019 20:56

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