Performance Evaluation of Kernels in Support Vector Machine
Intisar Shadeed Al-Mejibli, Dhafar Hamed Abd, Jwan K. Alwan, Abubaker Jumaah Rabash · 2018
Recently, the Support Vector Machine (SVM) algorithm becomes very common technique that developed for pattern classification. This technique has been employed in many fields such as bioinformatics and with different attributes of data sets for instance numeric, nominal or mixed. One of the significant issues that user faces when implementing the SVM is choosing the appropriate kernel function with attributes of data set to be investigated. This paper studied the behavior of SVM in regarding to the used attributes of dataset with different kernel functions. It analyzed the influence of various datasets descriptions on efficiency of (SVM)classification.SVM with these kernels have been implemented in Matlab. The investigated kernel functions are linear, polynomials, Sigmoid and Radial Based Function (RBF) . The evaluation process shows that the description of dataset with the used kernel function affects the performance of SVM classifier. Generally, SVM with linear and RBF achieved 100% in classification process when Mushroom dataset is used, and 99% when Sickle Cell Disease (SCD) is used.