Iteratively local fisher score for feature selection

In machine learning, feature selection is a kind of important dimension reduction techniques, which aims to choose features with the best discriminant ability to avoid the issue of curse of dimensionality for subsequent processing. As a supervised feature selection method, Fisher score (FS) provides a feature evaluation criterion and has been widely used. However, FS ignores the association between features by assessing all features independently and loses the local information for fully connecting within-class samples. In order to solve these issues, this paper proposes a novel feature evaluation criterion based on FS, named iteratively local Fisher score (ILFS). Compared with FS, the new criterion pays more attention to the local structure of data by using K nearest neighbours instead of all samples when calculating the scatters of within-class and between-class. In order to consider the relationship between features, we calculate local Fisher scores of feature subsets instead of scores of single features, and iteratively select the current optimal feature to achieve this idea like sequential forward selection (SFS). Experimental results on UCI and TEP data sets show that the improved algorithm performs well in classification activities compared with some other state-of-the-art methods.