Support Vector Machine (SVM) is one of the significant classification technique and it can be applied in various areas like meteorology, financial data analysis etc. The performance of SVM is influenced by parameters like C, which is cost constant and kernel parameter. In this paper, an improved Ant Colony Optimization (IACO) technique is proposed to optimize the parameters of SVM. To evaluate the proposed approach, the experiment adopts five benchmark datasets. The developed approach was compared with the ACO-SVM algorithm proposed by Zhang et al. The experimental results of the simulation show that performance of the proposed method is encouraging.
Rongali, S., & Yalavarthi, R. (2017). An Improved Ant Colony Optimization for Parameter Optimization using Support Vector Machine. International Journal of Engineering and Advanced Technology (IJEAT), 6(3), 198–204. Retrieved from https://www.ijeat.org/v6i3.php