Incremental continuous ant colony optimization For tuning support vector machine ’ s parameters

Support Vector Machines are considered to be excellent patterns classification techniques. The process of classifying a pattern with high classification accuracy counts mainly on tuning Support Vector Machine parameters which are the generalization error parameter and the kernel function parameter. Tuning these parameters is a complex process and Ant Colony Optimization can be used to overcome the difficulty. Ant Colony Optimization originally deals with discrete optimization problems. Hence, in applying Ant Colony Optimization for optimizing Support Vector Machine parameters, which are continuous in nature, the values wil have to be discretized. The discretization process will result in loss of some information and, hence, affects the classification accuracy and seeks time. This paper presents an algorithm to optimize Support Vector Machine parameters using Incremental continuous Ant Colony Optimization without the need to discretize continuous values. Eight datasets from UCI were used to evaluate the performance of the proposed algorithm. The proposed algorithm demonstrates the credibility in terms of classification accuracy when compared to grid search techniques, GAwith feature chromosome-SVM, PSO-SVM, and GA-SVM. Experimental results of the proposed algorithm also show promising performance in terms of classification accuracy and size of features subset. Keywords—Incremental continuous ant colony optimization, model selection, pattern classification, probability density function, support vector machine.