Embedded feature ranking for ensemble MLP classifiers
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Terry Windeatt1, Rakkrit Duangsoithong, Raymond Smith
1Centre for Vision Speech and Signal Processing, Faculty of Electronics and Physical Sciences, University of Surrey, Guildford Surrey, UK. t.windeatt@surrey.ac.uk
IEEE Transactions on Neural Networks
|May 25, 2011
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.
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