- Title
- Parameter optimization for Support Vector Machine Classifier with IO-GA
- Creator
- Zhou, Jing; Maruatona, Omaru; Wang, Wei
- Date
- 2011
- Type
- Text; Conference proceedings
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/64835
- Identifier
- vital:4598
- Identifier
-
https://doi.org/10.1109/IWCDM.2011.34
- Abstract
- The Support Vector Machine method has a good learning and generalization ability. Unfortunately, there are no comprehensive theories to guide the parameter selection of the SVM, which largely limits its application. In order to get the optimal parameters automatically, researchers have tried a variety of methods. Using genetic algorithms to optimize parameters of an SVM Classifier has become one of the favorite methods in recent years. In this paper, we explain how the Standard Genetic Algorithm (SGA) causes the problem of premature convergence and limits the accuracy of the SVM. We also put forward a new genetic algorithm with improved genetic operators (IO-GA) to optimize the SVM classifier's parameters. Experimental results show that the parameters obtained by this method can greatly improve the classification performance of SVM. We therefore conclude that this method is effective. © 2011 IEEE.
- Publisher
- Nanjing, Jiangsu IEEE
- Rights
- IEEE
- Rights
- This metadata is freely available under a CCO license
- Subject
- Genetic algorithm; Parameters optimization; Support Vector Machine; Classification performance; Generalization ability; Genetic operators; Optimal parameter; Parameter optimization; Parameter selection; Premature convergence; Standard genetic algorithm; Support vector; Support vector machine method; SVM classifiers; Data mining; Genetic algorithms; Optimization; Parameter estimation; Support vector machines
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