Difference between revisions of "Publications:Feature Selection with Neural Networks"
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| − | |Name=Verikas, Antanas | + | |Name=Verikas, Antanas (av) (0000-0003-2185-8973) (Högskolan i Halmstad (2804), Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) (3905), Halmstad Embedded and Intelligent Systems Research (EIS) (3938));Bacauskiene, Marija (Department of Applied Electronics, Kaunas University of Technology, LT-3031, Kaunas, Lithuania) |
|Title=Feature Selection with Neural Networks | |Title=Feature Selection with Neural Networks | ||
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|diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:235620}} | |diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:235620}} | ||
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Latest revision as of 21:39, 30 September 2016
| 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. |
