An ACO-based algorithm for parameter optimization of support vector machines

• Published in: Expert systems with applications Vol. 37; no. 9; pp. 6618 - 6628
• Main Authors: Zhang, XiaoLi, Chen, XueFeng, He, ZhengJia
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2010
• Subjects: Acceptability; ACO-SVM model; Algorithms; Ant colony optimization (ACO) algorithm; Benchmarking; Expert systems; Feasibility; Mathematical models; Optimization; Parameter optimization; Support vector machines; Support vector machines (SVM); ACO-SVM model; Parameter optimization; Support vector machines (SVM); Ant colony optimization (ACO) algorithm
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2010.03.067

One of the significant research problems in support vector machines (SVM) is the selection of optimal parameters that can establish an efficient SVM so as to attain desired output with an acceptable level of accuracy. The present study adopts ant colony optimization (ACO) algorithm to develop a novel ACO-SVM model to solve this problem. The proposed algorithm is applied on some real world benchmark datasets to validate the feasibility and efficiency, which shows that the new ACO-SVM model can yield promising results.