SVM RBF Parameters Testing Optimization Using Cross Validation and Grid Search to Improve Multiclass Classification
The accuracy of using optimal parameter values in kernel functions is as a determinant to obtain maximum accuracy results on Image retrieval with Support Vector Machine (SVM) classification. Experiments conducted in this study aimed to obtain optimal Gaussian Radial Basis Function (RBF) kernel function parameter values on non linear multi class Support Vector Machine (SVM) method. Cross Validation and Grid Search methods were applied in analyzing and testing the optimization range of SVM RBF kernel parameter values to recognize the image of Indonesian traditional Batik which has geometric decorative patterns. In addition, a feature dataset of Batik images from the results of Discrete Wavelet Transform (DWT) level 3 db2 was used in this study. The feature dataset was used as training and test dataset. By using Cross validation and Grid Search, it resulted in the range value of parameter C {26.5, 26.75, 27, 27.25, 27.5, 27.75, 28} and g {2 14.5, 2 14.75, 2 15, 2 15.25, 2 15.5, 2 15.75, 2 16} and the accuracy value of maximum classification for parameter C 27 and g=2 15. These range results of parameter values and optimal parameter values can be used as a reference in applying parameters on image recognition with geometric decorative motif texture using SVM RBF kernel classification.