Embedded feature ranking for ensemble MLP classifiers
Windeatt, T, Duangsoithong, R and Smith, R (2011) Embedded feature ranking for ensemble MLP classifiers IEEE Transactions on Neural Networks, 22 (6). pp. 988-994.
windeatt_transnnsh3.pdf - Accepted version Manuscript
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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.
|Divisions :||Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing|
|Identification Number :||https://doi.org/10.1109/TNN.2011.2138158|
|Depositing User :||Symplectic Elements|
|Date Deposited :||22 Jul 2011 11:02|
|Last Modified :||08 Nov 2013 12:09|
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