Discretization Algorithm Based on Particle Swarm Optimization and Its Application in Attributes Reduction for Fault Data

• Published in: Shanghai jiao tong da xue xue bao Vol. 23; no. 5; pp. 691 - 695
• Main Authors: Zheng, Bo, Li, Yanfeng, Fu, Guozhong
• Format: Journal Article
• Language: English
• Published: Shanghai Shanghai Jiaotong University Press 01.10.2018; Springer Nature B.V
• Subjects: Algorithms; Architecture; Computer Science; Data mining; Data processing; Discretization; Electrical Engineering; Engineering; Entropy; Fault diagnosis; Life Sciences; Materials Science; Mathematical analysis; Particle swarm optimization; Reduction; attributes discretization; TH 17; discernibility matrix; fault data reduction; TP 181; particle swarm optimization (PSO); hybrid weight
• ISSN: 1007-1172; 1995-8188
• DOI: 10.1007/s12204-018-1964-3

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.