Embedded Feature Ranking for Ensemble MLP Classifiers

@article{Windeatt2011EmbeddedFR,
  title={Embedded Feature Ranking for Ensemble MLP Classifiers},
  author={Terry Windeatt and Rakkrit Duangsoithong and Raymond S. Smith},
  journal={IEEE Transactions on Neural Networks},
  year={2011},
  volume={22},
  pages={988-994}
}
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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