Selecting features for neural network committees

We present a neural network based approach for identifying salient features for classification in 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 the output sensitivity to 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 two other neural network based feature selection methods. The algorithm developed outperforms the methods by achieved a higher classification accuracy on three real world problems tested.