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一种用于多分类问题的改进支持向量机 被引量：18

Improved support vector machine for multi-class classification problems
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摘要 针对非均衡分布的多类分类问题,为提高支持向量机(SVM)算法的性能,提出了一种改进的SVM算法.将遗传算法(GA)与传统SVM算法结合,构造出一种参数最优的进化SVM(GA SVM),SVM模型采用径向基函数(RBF)作为核函数,利用格雷码编码方式对SVM算法的模型参数进行遗传编码和优化搜索,将搜索到的优化结果作为SVM的最终模型参数.在两个不同特性的数据集上进行仿真测试,结果表明,与使用交叉验证策略的简单SVM相比,改进后的GA SVM算法在多类非均衡问题上明显提高了分类正确率,学习速度也有提高. To improve the performance of traditional support vector machine (SVM) on the dataset with unbalanced class distribution, an improved SVM was presented. Genetic algorithm-SVM (GA-SVM) was constructed by combining the genetic algorithm and the simple support vector machine. The parameters of SVM were coded into chromosomes with Gray coding strategy. After optimally searching in the parameter space, the outcome was used as the final parameters of SVM. GA-SVM was tested on two far different datasets from machine learning repository. Compared with the standard SVM with cross validation strategy, the simulation results indicated the superiority of GA-SVM on the dataset with unbalanced classes. GA-SVM could gain higher classification accurate and faster learning speed, and work well with faster learning speed on the perfectly constructed dataset.
作者 黄景涛 马龙华 钱积新
机构地区 浙江大学工业控制技术国家重点实验室系统工程研究所
出处 《浙江大学学报（工学版）》 EI CAS CSCD 北大核心 2004年第12期1633-1636,1659,共5页 Journal of Zhejiang University：Engineering Science
基金 国家自然科学基金资助项目(60474064) 国家"973"重点基础研究发展规划资助项目(2002CB312200).
关键词 系统工程 支持向量机 遗传算法 进化支持向量机 Genetic algorithms Learning algorithms Systems engineering Water treatment
分类号 TP181 [自动化与计算机技术—控制理论与控制工程]
  • 相关文献

参考文献10

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二级参考文献22

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共引文献120

同被引文献135

引证文献18

二级引证文献117

浙江大学学报（工学版）
2004年 第12期

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