Feature selection based on scatter degree
Jun-Ling Xu, Baowen Xu, Cong Wang, Zi-Feng Cui · 2008
Feature selection is an important task in machine learning, pattern recognition and data mining. This paper proposed a new feature selection method for classification, named SD, which is based on scatter matrix used in linear discriminant analysis. The main feature of SD is its simplicity and independency of learning algorithms. High-dimensional data samples are first projected into a lower dimensional subspace of the original feature space by means of a linear transformation matrix, which can be attained according to the scatter degree of each feature, and then the scatter degree is used to measure the importance of each feature. A comparison of SD and some popular feature selection methods (information gain and chi2-test) is conducted, and the results of experiment carried out on 19 data sets show the advantages of SD.