A fast supervised method of feature ranking and selection for pattern classification

This paper describes a fast, non-parametric algorithm for feature ranking and selection for better classification accuracy. In real world cases, some of the features are noisy or redundant, which leads to the question - which features must be selected to obtain the best classification accuracy? We propose a supervised feature selection method, where features forming distinct class-wise distributions are given preference. Number of features selected for final classification is adaptive, but depends on the dataset used for training. We validate our proposed method by comparing with an existing method using real world datasets. © 2009 Springer-Verlag Berlin Heidelberg.