| Support Vector Machine Ensemble Based on Genetic Algorithm |
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| 作者姓名： | 李烨 尹汝泼 蔡云泽 许晓鸣 |
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| 作者单位： | Department of Automation, Shanghai Jiaotong University, Shanghai 200030 |
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| 基金项目： | 国家重点基础研究发展计划(973计划);国家自然科学基金 |
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| 摘 要： | Support vector machines （SVMs） have been introduced as effective methods for solving classification problems. However, due to some limitations in practical applications, their generalization performance is sometimes far from the expected level. Therefore, it is meaningful to study SVM ensemble learning. In this paper, a novel genetic algorithm based ensemble learning method, namely Direct Genetic Ensemble （DGE）, is proposed. DGE adopts the predictive accuracy of ensemble as the fitness function and searches a good ensemble from the ensemble space. In essence, DGE is also a selective ensemble learning method because the base classifiers of the ensemble are selected according to the solution of genetic algorithm. In comparison with other ensemble learning methods, DGE works on a higher level and is more direct. Different strategies of constructing diverse base classifiers can be utilized in DGE. Experimental results show that SVM ensembles constructed by DGE can achieve better performance than single SVMs, hagged and boosted SVM ensembles. In addition, some valuable conclusions are obtained.
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| 关 键 词： | 系综学习 遗传算法 支撑向量机器 多样性 |
| 收稿时间： | 2005-01-11 |
Support Vector Machine Ensemble Based on Genetic Algorithm |
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| LI Ye,YIN Ru-po,CAI Yun-ze,XU Xiao-ming.Support Vector Machine Ensemble Based on Genetic Algorithm[J].Journal of Donghua University,2006,23(2):74-79. |
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| Authors: | LI Ye YIN Ru-po CAI Yun-ze XU Xiao-ming |
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| Abstract: | Support vector machines (SVMs) have been introduced as effective methods for solving classification problems. However, due to some limitations in practical applications, their generalization performance is sometimes far from the expected level. Therefore, it is meaningful to study SVM ensemble learning. In this paper, a novel genetic algorithm based ensemble learning method, namely Direct Genetic Ensemble (DGE), is proposed. DGE adopts the predictive accuracy of ensemble as the fitness function and searches a good ensemble from the ensemble space. In essence, DGE is also a selective ensemble learning method because the base classifiers of the ensemble are selected according to the solution of genetic algorithm. In comparison with other ensemble learning methods, DGE works on a higher level and is more direct. Different strategies of constructing diverse base classifiers can be utilized in DGE. Experimental results show that SVM ensembles constructed by DGE can achieve better performance than single SVMs, bagged and boosted SVM ensembles. In addition, some valuable conclusions are obtained. |
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| Keywords: | ensemble learning genetic algorithm support vector machine diversity |
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