]> 2026-04-04T20:35:06+02:00 Publications:Selecting features with neural networks 500 en <p>We present a neural network based approach for identifying salient features for classification in feed-forward 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 compared the approach with five other feature selection methods, each of which banks on different concept. The algorithm developed outperformed the other methods by achieving a higher classification accuracy on all the problems tested.</p> 8th International Conference on Neural Information Processing (ICONIP 2001), SHANGHAI, PEOPLES R CHINA, NOV 14-18, 2001 http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:545121 68 Neural Information Precessing : ICONIP-2001 proceedings Conference Paper 63 Selecting features with neural networks 2001 2016-09-30T20:41:28Z 2457662.3621296 Selecting features with neural networks 500 0 plainlist list 1 [[:Publications:Selecting features with neural networks]] Selecting features with neural networks# QUERYbc9715e2668ff0159173dae4191bae6b 500 [2,"smw-datavalue-property-restricted-annotation-use","Publisher"] Selecting features with neural networks# ERR6c155b5d70b69ddb590b4f2e73389c2a 500 [2,"smw-datavalue-property-restricted-annotation-use","Author"] Selecting features with neural networks# ERRe24f17e090c98448f3de99d03182b7b6