Difference between revisions of "Publications:Selecting salient features for classification committees"
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Latest revision as of 07:48, 7 April 2018
| Title | Selecting salient features for classification committees |
|---|---|
| Author | Antanas Verikas and Marija Bacauskiene and Kerstin Malmqvist |
| Year | 2003 |
| PublicationType | Conference Paper |
| Journal | |
| HostPublication | Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003 |
| Conference | Joint International Conference on Artificial Neural Networks (ICANN)/International on Neural Information Processing (ICONIP), JUN 26-29, 2002, ISTANBUL, TURKEY |
| DOI | http://dx.doi.org/10.1007/3-540-44989-2_5 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:1195746 |
| Abstract | We present a neural network based approach for identifying salient features for classification in neural network committees. Our approach involves neural network training with an augmented cross-entropy error function. The augmented error function forces the neural network to keep low derivatives of the transfer functions of neurons of the network when learning a classification task. Feature selection is based on two criteria, namely the reaction of the cross-validation data set classification error due to the removal of the individual features and the diversity of neural networks comprising the committee. The algorithm developed removed a large number of features from the original data sets without reducing the classification accuracy of the committees. By contrast, the accuracy of the committees utilizing the reduced feature sets was higher than those exploiting all the original features. © Springer-Verlag Berlin Heidelberg 2003. |
