Optimization of SVM Parameters with Hybrid CS-PSO Algorithms for Parkinson’s Disease in LabVIEW Environment

Autor: Duygu Kaya
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Zdroj: Parkinson's Disease, Vol 2019 (2019)
Druh dokumentu: article
ISSN: 2090-8083
2042-0080
DOI: 10.1155/2019/2513053
Popis: 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.
Databáze: Directory of Open Access Journals
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