Selection of variables for neural network analysis Comparisons of several methods with high energy physics data

Abstract

This paper compares five different methods for selecting the most important variables with a view to classify high energy physics events with neural networks. The different methods are: the F-test, principal component analysis (PCA), a decision tree method: CART, weight evaluation, and optimal cell damage (OCD). The neural networks use the variables selected with the different methods. We compare the percentages of events properly classified by each neural network. The learning set and the test set are the same for all the neural networks.