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A fast linear-in-the-parameters classifier construction algorithm using orthogonal forward selection to minimize leave-one-out misclassification rate

A fast linear-in-the-parameters classifier construction algorithm using orthogonal forward selection to minimize leave-one-out misclassification rate
A fast linear-in-the-parameters classifier construction algorithm using orthogonal forward selection to minimize leave-one-out misclassification rate
We propose a simple and computationally efficient construction algorithm for two class linear-in-the-parameters classifiers. In order to optimize model generalization, a forward orthogonal selection (OFS) procedure is used for minimizing the leave-one-out (LOO) misclassification rate directly. An analytic formula and a set of forward recursive updating formula of the LOO misclassification rate are developed and applied in the proposed algorithm. Numerical examples are used to demonstrate that the proposed algorithm is an
excellent alternative approach to construct sparse two class classifiers in terms of performance and computational efficiency.
0020-7721
119–125
Hong, Xia
e2869895-2015-4f79-a624-f994027ed12a
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Harris, Chris J.
dc305347-9cb2-4621-b42f-3f9950116e0d
Hong, Xia
e2869895-2015-4f79-a624-f994027ed12a
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Harris, Chris J.
dc305347-9cb2-4621-b42f-3f9950116e0d

Hong, Xia, Chen, Sheng and Harris, Chris J. (2008) A fast linear-in-the-parameters classifier construction algorithm using orthogonal forward selection to minimize leave-one-out misclassification rate. International Journal of Systems Science, 39 (2), 119–125.

Record type: Article

Abstract

We propose a simple and computationally efficient construction algorithm for two class linear-in-the-parameters classifiers. In order to optimize model generalization, a forward orthogonal selection (OFS) procedure is used for minimizing the leave-one-out (LOO) misclassification rate directly. An analytic formula and a set of forward recursive updating formula of the LOO misclassification rate are developed and applied in the proposed algorithm. Numerical examples are used to demonstrate that the proposed algorithm is an
excellent alternative approach to construct sparse two class classifiers in terms of performance and computational efficiency.

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More information

Published date: February 2008
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 265096
URI: http://eprints.soton.ac.uk/id/eprint/265096
ISSN: 0020-7721
PURE UUID: 348194a3-d443-4644-96a7-1c8693ffbcfc

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Date deposited: 23 Jan 2008 09:21
Last modified: 21 Apr 2021 16:32

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