期刊
PATTERN RECOGNITION LETTERS
卷 23, 期 11, 页码 1323-1335出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/S0167-8655(02)00081-8
关键词
classification; neural network; feature selection; regularization
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. (C) 2002 Elsevier Science B.V. All rights reserved.
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