Model selection of SVMs using GA approach

• Published in: 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541) Vol. 3; pp. 2035 - 2040 vol.3
• Main Authors: CHEN, Peng-Wei, WANG, Jung-Ying, LEE, Hahn-Ming
• Format: Conference Proceeding
• Language: English
• Published: Piscataway NJ IEEE 2004
• Subjects: Applied sciences; Artificial intelligence; Biological cells; Computer science; Computer science; control theory; systems; Costs; Electronic mail; Encoding; Exact sciences and technology; Genetic algorithms; Kernel; Machine learning; Stability; Support vector machines; Statistical analysis; Genetic algorithm; Objective analysis; Vector support machine; Objective function; Neural network; Modeling; Search algorithm
• ISBN: 0780383591; 9780780383593
• ISSN: 1098-7576
• DOI: 10.1109/IJCNN.2004.1380929

A new automatic search methodology for model selection of support vector machines, based on the GA-based tuning algorithm, is proposed to search for the adequate hyperparameters of SVMs. In our method, each chromosome indicates a group of hyperparameters, and the population is a collection of chromosomes. Experimental results show that our method performs superiorly on time cost, performance and stability. Our algorithm requires only the evaluation of an objective function to guide its search with no additional derivative or auxiliary knowledge required. In addition, the encoding of chromosomes makes the implementation of multiple hyperparameters tuning simpler.