Feature weighting and SVM parameters optimization based on genetic algorithms for classification problems

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• 作者：Anh Viet Phan ; Minh Le Nguyen ; Lam Thu Bui
• 关键词：Genetic algorithms (GAs) ; Support vector machines (SVMs) ; Classification ; Feature weighting ; Feature selection
• 刊名：Applied Intelligence
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：46
• 期：2
• 页码：455-469
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence (incl. Robotics); Mechanical Engineering; Manufacturing, Machines, Tools;
• 出版者：Springer US
• ISSN：1573-7497
• 卷排序：46

文摘

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.