J Shanghai Jiaotong Univ Sci

Journal of Shanghai Jiaotong University(Science) >

2018 , Vol. 23 >Issue 5: 691 - 695

DOI: https://doi.org/10.1007/s12204-018-1964-3

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

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  • (1. Aviation Engineering institution, Civil Aviation Flight University of China, Guanghan 618307, Sichuan, China; 2. Center for System Reliability and Safety, School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China)

Online published: 2018-10-07

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Abstract

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.

Key words： attributes discretization; fault data reduction; discernibility matrix; particle swarm optimization (PSO); hybrid weight

Cite this article

ZHENG Bo (郑波), LI Yanfeng (李彦锋), FU Guozhong (付国忠) . Discretization Algorithm Based on Particle Swarm Optimization and Its Application in Attributes Reduction for Fault Data[J]. Journal of Shanghai Jiaotong University(Science), 2018 , 23(5) : 691 -695 . DOI: 10.1007/s12204-018-1964-3

References

[1] TAO Z, XU B D, WANG D W, et al. Rough set knowledgereduction approach based on GA [J]. Systems Enginnering,2003, 21(4): 116-122 (in Chinese). [2] YI Z, LI W S. Intrusion detection method based onrough set and genetic reduction algorithm [J]. ComputerEngineering and Application, 2010, 46(21): 116-118 (in Chinese). [3] DING W P, WANG J D, GUAN Z J. Cooperative extendedrough attribute reduction algorithm based onimproved PSO [J]. Journal of Systems Engineering andElectronics, 2012, 23(1): 160-166. [4] ZHAO B T, CHEN X J, ZENG Q S. Hybrid attributesreduction based on neighborhood granulationand niche PSO algorithm [J]. Systems Engineering andElectronics, 2010, 32(12): 2603-2607 (in Chinese). [5] ZHENG B. Research of aero-engine fault diagnosisbased on rough neural network [D]. Guanghan,Sichuang: Civial Aviation Flight University of China,2009 (in Chinese). [6] TAO G, YAN Y G, LIU J, et al. Discrimination of continuousattributes based on improved SOM clustering[J]. Journal of Computer Application, 2015, 35(S1):89-92 (in Chinese). [7] KURGAN L A, CIOS K J. CAIM discretization algorithm[J]. IEEE Transactions on Knowledge and DataEngineering, 2004, 16(2): 145-153. [8] LI H, YAN D C, ZHANG Y C. Modified algorithm ofCAIM [J]. Computer Engineering, 2010, 36(4): 77-81(in Chinese). [9] ZHANG P F, LI B W, QIN M, et al. A method of continuousattributes discretization based on entropy [J].Gas Turbine Experiment and Research, 2014, 27(6):49-52 (in Chinese).
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