University of Surrey

Test tubes in the lab Research in the ATI Dance Research

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). 988 - 994. ISSN 1045-9227

windeatt_transnnsh3.pdf - Accepted Version
Available under License : See the attached licence file.

Download (504kB)
[img] Plain Text (licence)

Download (1kB)


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
Depositing User: Symplectic Elements
Date Deposited: 22 Jul 2011 11:02
Last Modified: 08 Nov 2013 12:09

Actions (login required)

View Item View Item


Downloads per month over past year

Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800