摘要
In order to increase the fault diagnosis efficiency and make the fault data mining be realized, the decision table containing numerical attributes must be discretized for further calculations. The discernibility matrix-based reduction method depends on whether the numerical attributes can be properly discretized or not.So a discretization algorithm based on particle swarm optimization(PSO) is proposed. Moreover, hybrid weights are adopted in the process of particles evolution. Comparative calculations for certain equipment are completed to demonstrate the effectiveness of the proposed algorithm. The results indicate that the proposed algorithm has better performance than other popular algorithms such as class-attribute interdependence maximization(CAIM)discretization method and entropy-based discretization method.
In order to increase the fault diagnosis efficiency and make the fault data mining be realized, the decision table containing numerical attributes must be discretized for further calculations. The discernibility matrix-based reduction method depends on whether the numerical attributes can be properly discretized or not. So a discretization algorithm based on particle swarm optimization （PSO） is proposed. Moreover, hybrid weights are adopted in the process of particles evolution. Comparative calculations for certain equipment are completed to demonstrate the effectiveness of the proposed algorithm. The results indicate that the proposed algorithm has better performance than other popular algorithms such as class-attribute interdependence maximization （CAIM） discretization method and entropy-based discretization method.
作者
郑波
李彦锋
付国忠
ZHENG Bo;LI Yanfeng;FU Guozhong(Aviation Engineering institution,Civil Aviation Flight University of China,Guanghan 618307,Sichuan,China;Center for System Reliability and Safety,School of Mechanical and Electrical Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China)
基金
the National Natural Science Foundation of China(No.51775090)
the General Program of Civil Aviation Flight University of China(No.J2015-39)
