Parameter selection of support vector machines and genetic algorithm based on change area search
- 3 May 2011
- journal article
- Published by Springer Science and Business Media LLC in Neural Computing & Applications
- Vol. 21 (1), 1-8
- https://doi.org/10.1007/s00521-011-0603-9
Abstract
Generalization performance of support vector machines (SVM) with Gaussian kernel is influenced by its model parameters, both the error penalty parameter and the Gaussian kernel parameter. After researching the characteristics and properties of the parameter simultaneous variation of support vector machines with Gaussian kernel by the parameter analysis table, a new area distribution model is proposed, which consists of optimal straight line, reference point of area boundary, optimal area, transition area, underfitting area, and overfitting area. In order to improve classification performance of support vector machines, a genetic algorithm based on change area search is proposed. Comparison experiments show that the test accuracy of the genetic algorithm based on change area search is better than that of the two-linear search method.Keywords
This publication has 9 references indexed in Scilit:
- An evolutionary approach for gene selection and classification of microarray data based on SVM error-bound theoriesBiosystems, 2010
- Evolutionary Computing Optimization for Parameter Determination and Feature Selection of Support Vector MachinesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2009
- Gene selection using genetic algorithm and support vectors machinesSoft Computing, 2008
- Particle swarm optimization for parameter determination and feature selection of support vector machinesExpert Systems with Applications, 2007
- ESVM: Evolutionary support vector machine for automatic feature selection and classification of microarray dataBiosystems, 2007
- A GA-based feature selection and parameters optimizationfor support vector machinesExpert Systems with Applications, 2006
- Asymptotic Behaviors of Support Vector Machines with Gaussian KernelNeural Computation, 2003
- Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithmsIEEE Transactions on Neural Networks, 2002
- Choosing Multiple Parameters for Support Vector MachinesMachine Learning, 2002