title: Embedded feature ranking for ensemble MLP classifiers.
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.
authors: Centre for Vision Speech and Signal Processing, Faculty of Electronics and Physical Sciences, University of Surrey, Guildford Surrey, UK. t.windeatt@surrey.ac.uk
authors: Terry
authors: T
authors: Windeatt
authors: Rakkrit
authors: R
authors: Duangsoithong
authors: Raymond
authors: R
authors: Smith