Performance comparison of several feature selection methods based on node pruning in handwritten character recognition

The paper presents a performance comparison of several feature selection methods based on neural network node pruning. Assuming the features are extracted and presented as the inputs of a 3 layered perceptron classifier, we apply the five feature selection methods before/during/after neural network training in order to prune only input nodes of the neural network. Four of them are node pruning methods such as node saliency method, node sensitivity method, and two interactive pruning methods using different contribution measures. The last one is a statistical method based on principle component analysis (PCA). The first two of them prune input nodes during training whereas the last three do before/after network training. For gradient and upper down, left right hole concavity features, we perform several experiments of handwritten English alphabet and digit recognition with/without pruning using the five feature selection algorithms, respectively. The experimental results show that node saliency method outperforms the others.