Feature selection based on FDA and F-score for multi-class classification

F-score is a simple feature selection technique, however, it works only for two classes. This paper proposes a novel feature ranking method based on Fisher discriminate analysis (FDA) and F-score, denoted as FDAF-score, which considers the relative distribution of classes in a multi-dimensional feature space. The main idea is that a proper subset is got according to maximizing the proportion of average between-class distance to the relative within-class scatter. Because the method removes all insignificant features at a time, it can effectively reduce computational cost. Experiments on six benchmarking UCI datasets and two artificial datasets demonstrate that the proposed FDAF-score algorithm can not only obtain good results with fewer features than the original datasets as well as fast computation but also deal with the classification problem with noises well.