Cairo University, Faculty of Computers & Information Information Technology Department 5 Ahmed Zewal St., Orman, Giza, office box# 12613 (email)
Support Vector Machine (SVM) is one of the well-known classifiers. SVM parameters such as kernel
parameters and penalty parameter (C) significantly influences the classification accuracy. In this
paper, a novel Chaotic Antlion Optimization (CALO) algorithm has been proposed to optimize the
parameters of SVM classifier, so that the classification error can be reduced. To evaluate the proposed
model (CALO-SVM), the experiment adopted six standard datasets which are obtained from UCI machine
learning data repository. For verification, the results of the CALO-SVM algorithm are compared
with grid search, which is a conventional method of searching parameter values, standard Ant Lion
Optimization (ALO) SVM, and two well-known optimization algorithms: Genetic algorithm (GA)
and Particle Swarm Optimization (PSO). The experimental results proved that the proposed model is
capable to find the optimal values of the SVM parameters and avoids the local optima problem. The
results also demonstrated lower classification error rates compared with GA and PSO algorithms