Efficient classification of chronic kidney disease by using multi‐kernel support vector machine and fruit fly optimization algorithm

2020 ◽  
Vol 30 (3) ◽  
pp. 660-673
Author(s):  
L. Jerlin Rubini ◽  
Eswaran Perumal
Keyword(s):  
Chronic Kidney Disease ◽  
Support Vector Machine ◽  
Kidney Disease ◽  
Optimization Algorithm ◽  
Fruit Fly ◽  
Support Vector ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Kernel Support Vector Machine

scholarly journals Fault diagnosis using improved pattern spectrum and fruit fly optimization algorithm–support vector machine

2018 ◽  
Vol 10 (11) ◽  
pp. 168781401881093 ◽  
Author(s):  
Bing Wang ◽  
Xiong Hu ◽  
Wei Wang ◽  
Dejian Sun
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Optimization Algorithm ◽  
Simulation Analysis ◽  
Roller Bearing ◽  
Fruit Fly ◽  
Support Vector ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Pattern Spectrum

A fault diagnosis method using improved pattern spectrum and fruit fly optimization algorithm–support vector machine is proposed. Improved pattern spectrum is introduced for feature extraction by employing morphological erosion operator. Simulation analysis is processed, and the improved pattern spectrum curves present a steady distinction feature and smaller calculating amount than pattern spectrum method. Support vector machine with fruit fly optimization algorithm which can help seeking optimal parameters is employed for pattern recognition. Experiments were conducted, and the proposed method is verified by roller bearing vibration data including different fault types. The classification accuracy of the proposed approach reaches 87.5% (21/24) in training and 91.7% (44/48) in testing, showing an acceptable diagnosis effect.

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