%0 Journal Article
%A LIU Jing
%A QIU Li-ke
%A SUN Zhong-wei
%A ZHAO Yang-fan
%T A Fast Feature Selection Framework and Method
%D 2019
%R 10.13190/j.jbupt.2018-151
%J Journal of Beijing University of Posts and Telecommunications
%P 127-132
%V 42
%N 3
%X Aiming at the imbalance between accuracy and computational efficiency in feature selection, a fast feature selection framework (FFFS) is proposed. Based on this framework, a fast feature selection algorithm, MRMR-SFS, is proposed. The minimum redundancy maximum relevance (MRMR) method is used to select the candidate features, and sequential forward selection (SFS) method is used to verify the performance of the candidate features as well. It improves the calculation efficiency by limiting the number of iterations. Comparison experiments with the MRMR, SFS and a filter-dominating hybrid sequential floating forward selection algorithms demonstrate that MRMR-SFS can balance the accuracy and computational efficiency well.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2018-151