Embedded feature ranking for ensemble MLP classifiers
Tools
Tools
Tools
Windeatt, T, Duangsoithong, R and Smith, R (2011) Embedded feature ranking for ensemble MLP classifiers IEEE Transactions on Neural Networks, 22 (6). 988 - 994. ISSN 1045-9227
![[img]](/style/images/fileicons/application_pdf.png)
| PDF - Accepted Version Available under License : See the attached licence file. 492Kb | |
![[img]](/style/images/fileicons/text_plain.png) | Plain Text (licence) 1516b |
Official URL: http://dx.doi.org/10.1109/TNN.2011.2138158
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.
| Item Type: | Article |
|---|---|
| Divisions: | Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing |
| ID Code: | 6484 |
| Deposited By: | Symplectic Elements |
| Deposited On: | 22 Jul 2011 12:02 |
| Last Modified: | 25 May 2013 14:45 |
Document Downloads
Repository Staff Only: item control page