PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Embedded feature ranking for ensemble MLP classifiers
Terry Windeatt, r Duangsoithong and r s smith
IEEE Trans NN Volume 22, Number 6, pp. 988-994, 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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:9149
Deposited By:Terry Windeatt
Deposited On:21 February 2012