RBF KERNEL OPTIMIZATION METHOD WITH PARTICLE SWARM OPTIMIZATION ON SVM USING THE ANALYSIS OF INPUT DATA’S MOVEMENT
Abstract: SVM (Support Vector
Machine) with RBF (Radial Basis Function) kernel is a frequently used
classification method because usually it provides an accurate results. The
focus about most SVM optimization research is the optimization of the the input
data, whereas the parameter of the kernel function (RBF), the sigma, which is
used in SVM also has the potential to improve the performance of SVM when
optimized. In this research, we proposed a new method of RBF kernel
optimization with Particle Swarm Optimization (PSO) on SVM using the analysis
of input data’s movement. This method performed the optimization of the weight
of the input data and RBF kernel’s parameter at once based on the analysis of
the movement of the input data which was separated from the process of
determining the margin on SVM. The steps of this method were the parameter
initialization, optimal particle search, kernel’s parameter computation, and
classification with SVM. In the optimal particle’s search, the cost of each
particle was computed using RBF function. The value of kernel’s parameter was
computed based on the particles’ movement in PSO. Experimental result on Breast
Cancer Wisconsin (Original) dataset showed that this RBF kernel optimization
method could improve the accuracy of SVM significantly. This method of RBF
kernel optimization had a lower complexity compared to another SVM optimization
methods that resulted in a faster running time.
Author: Rarasmaya Indraswari,
Agus Zainal Arifin
Journal Code: jptkomputergg170006
