Optimized Support Vector Machine for classifying infant cries with asphyxia using Orthogonal Least Square

2010 ◽  
Author(s):  
R. Sahak ◽  
Y. K. Lee ◽  
W. Mansor ◽  
A. I. M. Yassin ◽  
A. Zabidi
Keyword(s):  
Support Vector Machine ◽  
Least Square ◽  
Support Vector ◽  
Orthogonal Least Square ◽  
Infant Cries

scholarly journals Performance of Principal Component Analysis and Orthogonal Least Square on Optimized Feature Set in Classifying Asphyxiated Infant Cry Using Support Vector Machine

2018 ◽  
Vol 9 (1) ◽  
pp. 139
Author(s):  
R. Sahak ◽  
W. Mansor ◽  
Khuan Y. Lee ◽  
A. Zabidi
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Classification Accuracy ◽  
Computation Time ◽  
Principal Component ◽  
Component Analysis ◽  
Least Square ◽  
Support Vector ◽  
Infant Cry ◽  
Orthogonal Least Square

<p>An investigation into optimized support vector machine (SVM) integrated with principal component analysis (PCA) and orthogonal least square (OLS) in classifying asphyxiated infant cry was performed in this study. Three approaches were used in the classification; SVM, PCA-SVM, and OLS-SVM. Various numbers of features extracted from Mel-frequency Cepstral coefficient (MFCC) were tested to obtain the optimal parameters of SVM kernels. Once the optimal feature set is obtained, PCA and OLS selected the most significant features and the optimized SVM then classified the selected cry patterns. In PCA-SVM, eigenvalue-one-criterion (EOC), cumulative percentage variance (CPV) and the Scree test (SCREE) were used to select the most significant features. SVM with radial basis function (RBF) kernel was chosen in the classification stage. The classification accuracy and computation time were computed to evaluate the performance of each method. The best method for classifying asphyxiated infant cry is PCA-SVM with EOC since it produces the highest classification accuracy which is 94.84%. Using PCA-SVM, the classification process was performed in 1.98s only. The results also show that employing feature selection techniques could enhance the classifier performance.</p>

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