A new decision criterion for feature selection application to the classification of non destructive testing signatures

This paper describes a new decision criterion for feature selection (or descriptor selection) and its application to a classification problem. The choice of representation space is essential in the framework of pattern recognition problems, especially when data is sparse, in which case the well-known curse of dimensionality appears inevitably [1]. Our method associates a ranking procedure based on Orthogonal Forward Regression with a new stopping criterion based on the addition of a random descriptor. It is applied to a non destructive rail diagnosis problem that has to assign each measured rail defect to one class among several ones.