Improved Classification Performance of Support Vector Machine Technique Using the Genetic Algorithm

In this research, the genetic algorithm was proposed as a method to find the parameters of support vector machine, specifically the and c parameters for kernel and the hyperplane respectively. Based on the Least squares method, the fitness function was built in the genetic algorithm to find the optimal values of the parameters in the proposed method. The proposed method showed better and more efficient results than the classical method of support vector machine which adopts the default or random values of parameters and c in the classification of leukemia data.