Optimization of SVM Parameters with Hybrid CS-PSO Algorithms for Parkinson’s Disease in LabVIEW Environment
Author
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-05-02
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Optimization is the process of achieving the best solution for a problem.
LabVIEW based on an SVM model is proposed in this paper to get the best SVM parameters using the hybrid CS and PSO method.
PCA is used as a preprocessor of SVM for reducing the dimension of data and extracting features of training samples.
Also, SVM parameters are optimized for Parkinson’s disease data by combining CS and PSO.
The designed system is used to determine the best SVM parameters, and it is compared to PSO and CS optimization methods and found that the used CS-PSO hybrid optimization method is better.
The hybrid model shows that the accuracy of the performance achieved is 97.4359%.
Also, the data classification results obtained by using SVM parameters determined by optimization are measured by precision, recall, F1 score, false positive rate (FPR), false discovery rate (FDR), false negative rate (FNR), negative predictive value (NPV), and Matthews’ correlation coefficient (MCC) parameters.
American Psychological Association (APA)
Kaya, Duygu. 2019. Optimization of SVM Parameters with Hybrid CS-PSO Algorithms for Parkinson’s Disease in LabVIEW Environment. Parkinson’s Disease،Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1206869
Modern Language Association (MLA)
Kaya, Duygu. Optimization of SVM Parameters with Hybrid CS-PSO Algorithms for Parkinson’s Disease in LabVIEW Environment. Parkinson’s Disease No. 2019 (2019), pp.1-9.
https://search.emarefa.net/detail/BIM-1206869
American Medical Association (AMA)
Kaya, Duygu. Optimization of SVM Parameters with Hybrid CS-PSO Algorithms for Parkinson’s Disease in LabVIEW Environment. Parkinson’s Disease. 2019. Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1206869
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1206869