Accuracy of feature selection and extraction in statistical and neural net pattern classification

Feature selection and feature extraction are common information processing stages in statistical pattern recognition and ANN classifier design. The number of samples used to evaluate the quality of feature subset and the use of simplified measures to speed up the evaluation procedures can cause a significant increase in a generalization error. Factors that determine the increase mentioned are analyzed and a method to determine this increase in practical work is proposed.