Publications:Feature Selection with Neural Networks
From ISLAB/CAISR
| Title | Feature Selection with Neural Networks |
|---|---|
| Author | Antanas Verikas and Marija Bacauskiene |
| Year | 2002 |
| PublicationType | Journal Paper |
| Journal | Pattern Recognition Letters |
| HostPublication | |
| Conference | |
| DOI | http://dx.doi.org/10.1016/S0167-8655(02)00081-8 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:235620 |
| Abstract | We present a neural network based approach for identifying salient features for classification in feedforward neural networks. 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 when learning a classification task. Such an approach reduces output sensitivity to the input changes. Feature selection is based on the reaction of the cross-validation data set classification error due to the removal of the individual features. We demonstrate the usefulness of the proposed approach on one artificial and three real-world classification problems. We compared the approach with five other feature selection methods, each of which banks on a different concept. The algorithm developed outperformed the other methods by achieving higher classification accuracy on all the problems tested. |
