Abstract:
By analyzing the impacts of Least Square Support Vector Machine(LS-SVM) model hyperparameter selection on the classifier, this paper proposes a method using estimation of distribution algorithms with diversity preservation named EDA-DP to optimally select model parameters of LS-SVM. Experiments are operated to recognize the benchmarks and radar High Range Resolution Profile(HRRP) datasets by using LS-SVM classifier. Compared to the grid-based method, the average recognition rate of LS-SVM classifier based on EDA-DP are increased by 4.2% and 1.76%. Experimental results demonstrate that the classifier model with EDA-DP achieves better classification ability and generalization capacity.
Key words:
Least Squares Support Vector Machine(LS-SVM),
optimal selection of model parameters,
Estimation of Distribution Algorithm with Diversity Preservation(EDA-DP),
target recognition,
Radial Basis Function(RBF)
摘要:
通过分析最小二乘支持向量机(LS-SVM)模型的超参数选择对分类器的影响,提出一种采用多样性保持的分布估计算法(EDA-DP)优化选择LS-SVM模型参数的方法。使用基于EDA-DP的LS-SVM分类器模型对基准数据集和雷达目标高分辨距离像数据集进行仿真实验,结果表明,该模型相比基于网格法的分类器模型,平均识别率分别提高了4.2%和1.76%,具有更好的分类性能和泛化能力。
关键词:
最小二乘支持向量机,
模型参数优化选择,
多样性保持的分布估计算法,
目标识别,
径向基函数
CLC Number:
XIONG Yang, XIAO Fu-Tie, WANG Wei. Optimal Selection of LS-SVM Parameter Based on EDA-DP[J]. Computer Engineering, 2011, 37(14): 146-148.
熊杨, 肖怀铁, 王伟. 基于EDA-DP的LS-SVM参数优化选择[J]. 计算机工程, 2011, 37(14): 146-148.