%0 Journal Article
%A PAN Su
%A LIU Zhan-feng
%T Fuzzy-Rough Bireducts Algorithm Based on Particle Swarm Optimization
%D 
%R 10.13190/j.jbupt.2020-237
%J Journal of Beijing University of Posts and Telecommunications
%P 49-55
%V 44
%N 4
%X Selecting informative features and removing noise instances are beneficial to gain a clean dataset and promote the performance of subsequent classifiers. A novel algorithm for fuzzy-rough bireducts with particle swarm optimization is proposed. The fitness function with <i>ε</i>-bireduct is employed to evaluate the candidate fuzzy-rough bireducts, which drives the particle swarm optimization search process toward better candidate solutions. The selected optimal bireduct is utilized to construct the subsequent classifier. The experimental results show that the proposed algorithm is superior to the counterpart, which reduces the instances and features effectively, and obtains high-quality bireducts. The classification accuracy of the proposed algorithm is thus better than the counterpart.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2020-237