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.
['Terry Windeatt', 'Rakkrit Duangsoithong', 'Raymond S. Smith']
Embedded Feature Ranking for Ensemble MLP Classifiers