Feature selection with neural networks

Authors:
A. Verikas;M. Bacauskiene
Affiliations:
Intelligent Systems Laboratory, Halmstad University, Box 823, S 301 18 Halmstad, Sweden and Department of Applied Electronics, Kaunas University of Technology, LT-3031, Kaunas, Lithuania;Department of Applied Electronics, Kaunas University of Technology, LT-3031, Kaunas, Lithuania
Venue:
Pattern Recognition Letters
Year:
2002

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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.