摘要
鉴于支持向量机特征选择和参数优化对其分类准确率有重大的影响,将支持向量机渐近性能融入遗传算法并生成特征染色体,从而将遗传算法的搜索导向超参数空间中的最佳化误差直线.在此基础上,提出一种新的基于带特征染色体遗传算法的方法,同时进行支持向量机特征选择和参数优化.在与网格搜索、不带特征染色体遗传算法和其他方法的比较中,所提出的方法具有较高的准确率、更小的特征子集和更少的处理时间.
The classification accuracy of support vector machines（SVM） depends on feature selection and parameter optimization of SVM strongly.The asymptotic behaviors of support vector machines are fused with genetic algorithm and the feature chromosomes are generated,which directs the search of genetic algorithm to the straight line of optimal generalization error in the superparameter space.On this basis,a new approach based on genetic algorithm with feature chromosomes is proposed to simultaneously optimize the feature subset and the parameters for SVM.Compared with the grid search,the genetic algorithm without feature chromosomes and other approaches,the proposed approach has higher classification accuracy,smaller feature subset and fewer processing time.
出处
《控制与决策》
EI
CSCD
北大核心
2010年第8期1133-1138,共6页
Control and Decision
基金
国家自然科学基金项目(60671033)
教育部博士点基金项目(20060614015)
关键词
特征染色体
遗传算法
特征选择
参数优化
支持向量机
Feature chromosomes
Genetic algorithm
Feature selection
Parameters optimization
Support vector machines
