KSVMKoret School of Veterinary Medicine (Israel)
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(d) 20 40 60 80 Linear SVM 80.56 90.79 90.62 90.79 KSVM 82.43 92.33 92.67 92.84 SRC 92.33 98.81 98.47 98.64 SRCA 93.01 99.66 99.83 99.66 Dims.
After training with ksvm function, the SVM is also tested using the test data set, which is remainder of the spam data set, excluding blogs in the training data set.
The weights/biases of kSVM are set as the variables, and the median square error (MSE) of the samples are set as the fitness function of PSO.
Here w and b are weights/biases of kSVM, respectively.
Traditional method uses trial-and-error to determine the optimal values of error penalty C and kernel parameter [sigma] of kSVMs. It will cause heavy computation burden, and cannot guarantee to find the optimal or even near-optimal solutions.
The results showed that the proposed DWT + PCA + KSVM method obtains quite excellent results on both training and validation images.
It indicates that our proposed method DWT + PCA + KSVM with GRB kernel performed best among the 10 methods, achieving the best classification accuracy as 99.38%.
In this study, we have developed a novel DWT + PCA + KSVM method to distinguish between normal and abnormal MRIs of the brain.
The proposed DWT + PCA + KSVM with GRB kernel method shows superiority to the LIN, HPOL, and IPOL kernels SVMs.
This technique of brain MRI classification based on PCA and KSVM is a potentially valuable tool to be used in computer assisted clinical diagnosis.
Confusion matrix of our DWT + PCA + KSVM method (Kernel chose LIN, HPOL, IPOL, and.