期刊
APPLIED INTELLIGENCE
卷 46, 期 2, 页码 455-469出版社
SPRINGER
DOI: 10.1007/s10489-016-0843-6
关键词
Genetic algorithms (GAs); Support vector machines (SVMs); Classification; Feature weighting; Feature selection
资金
- JSPS KAKENHI [3050941]
- Ministry of Training and Education (MOET), Vietnam [911]
- Vietnam National Foundation for Science and Technology Development (NAFOSTED) [102.01-2015.12]
- Grants-in-Aid for Scientific Research [15K16048] Funding Source: KAKEN
Support Vector Machines (SVMs) are widely known as an efficient supervised learning model for classification problems. However, the success of an SVM classifier depends on the perfect choice of its parameters as well as the structure of the data. Thus, the aim of this research is to simultaneously optimize the parameters and feature weighting in order to increase the strength of SVMs. We propose a novel hybrid model, the combination of genetic algorithms (GAs) and SVMs, for feature weighting and parameter optimization to solve classification problems efficiently. We call it as the GA-SVM model. Our GA is designed with a special direction-based crossover operator. Experiments were conducted on several real-world datasets using the proposed model and Grid Search, a traditional method of searching optimal parameters. The results show that the GA-SVM model achieves significant improvement in the performance of classification on all the datasets in comparison with Grid Search. In terms of accuracy, out method is competitive with some state-of-the-art techniques for feature selection and feature weighting.
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