]> 2026-04-05T00:56:33+02:00 Publications:Feature Selection with Neural Networks 500 en <p>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.</p> http://dx.doi.org/10.1016/S0167-8655(02)00081-8 http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:235620 1335 11 Pattern Recognition Letters Journal Paper 1323 Feature Selection with Neural Networks 23 2002 2016-09-30T20:39:58Z 2457662.361088 Feature Selection with Neural Networks 500 0 plainlist list 1 [[:Publications:Feature Selection with Neural Networks]] Feature Selection with Neural Networks# QUERYf326c46bf0bd418900507f5853a65149 500 [2,"smw-datavalue-property-restricted-annotation-use","Publisher"] Feature Selection with Neural Networks# ERR6c155b5d70b69ddb590b4f2e73389c2a 500 [2,"smw-datavalue-property-restricted-annotation-use","Author"] Feature Selection with Neural Networks# ERRe24f17e090c98448f3de99d03182b7b6