Title :
Embedded Feature Ranking for Ensemble MLP Classifiers
Author :
Windeatt, Terry ; Duangsoithong, Rakkrit ; Smith, Raymond
Author_Institution :
Centre for Vision Speech & Signal Process., Univ. of Surrey, Guildford, UK
fDate :
6/1/2011 12:00:00 AM
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.
Keywords :
learning (artificial intelligence); multilayer perceptrons; pattern classification; MLP base classifier; embedded feature ranking; error-correcting output coding; multiclass problem; multilayer perceptron ensemble; stopping criterion; Boosting; Decoding; Encoding; Error analysis; Noise; Static VAr compensators; Training; Classification; multilayer perceptrons; pattern analysis; pattern recognition; Algorithms; Computer Simulation; Models, Theoretical; Neural Networks (Computer); Pattern Recognition, Automated;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2011.2138158
