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Embedded feature ranking for ensemble MLP classifiers

Windeatt, T, Duangsoithong, R and Smith, R (2011) Embedded feature ranking for ensemble MLP classifiers IEEE Transactions on Neural Networks, 22 (6). 988 - 994. ISSN 1045-9227

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Abstract

A feature ranking scheme for multilayer perceptron (MLP) ensembles is proposed, along with a stopping criterion based upon the out-of-bootstrap estimate. To solve multi-class problems feature ranking is combined with modified error-correcting output coding. Experimental results on benchmark data demonstrate the versatility of the MLP base classifier in removing irrelevant features.

Item Type: Article
Divisions: Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Depositing User: Symplectic Elements
Date Deposited: 22 Jul 2011 11:02
Last Modified: 08 Nov 2013 12:09
URI: http://epubs.surrey.ac.uk/id/eprint/6484

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