Feature Selection For Neural Networks Using Parzen Density Estimator
Chulhee Lee, Jón Atli Benediktsson, D. A. Landgrebe · 2005
A feature selection method for neural networks is proposed using the Parzen density estimator. A new feature set is selected using the decision boundary feature selection algorithm. The selected feature set is then used to train a neural network. Using a reduced feature set, an attempt is made to reduce the training time of the neural network and obtain a simpler neural network, which further reduces the classification time for test data.