Feature selection approach based on discriminant analysis and neural networks
Yang Yang · Journal of Computer Applications · 2006
A new approach for feature selection based on discriminant analysis and regularization neural network was proposed.The neural network was trained by minimizing 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 reduced output sensitivity to the input changes.Firstly a feature queue in order could be obtained by using discriminant analysis based feature ranking.Feature selection was based on the reaction of the cross-validation data set classification error due to the removal of the individual features.The approach proposed was compared with four other feature selection methods,each of which banks on a different concept.The algorithm proposed outperforms the other methods by achieving higher classification accuracy on all the problems tested.