南京理工大学学报（自然科学版）2016，Vol.40Issue(1)：10-16,7.DOI:10.14177/j.cnki.32-1397n.2016.40.01.002
一种基于MA-LSSVM的封装式特征选择算法
Wrapper feature selection algorithm based on MA-LSSVM
摘要
Abstract
To improve the feature selection problem of the high dimensional small sample data,this paper combines memetic algorithm ( MA ) and least squares support vector machine ( LS-SVM ) to design a wrapper feature selection method ( MA-LSSVM ) . The solving strategy of the proposed method is composed by global search and local search,which utilizes the speciality of being easy to search optimal solution to construct classifiers and to regard classification accuracy as the main component of memetic algorithm fitness function in the optimization process. The experimental results demonstrate that the MA-LSSVM can be more efficient and stable to obtain features larger contribution to the classification precision, reducing the data dimension and improving the classification efficiency.关键词
特征选择/文化基因算法/最小二乘支持向量机/高维小样本数据/机器学习/全局搜索/局部搜索Key words
feature selection/memetic algorithm/least squares support vector machine/high dimensional small sample data/machine learning/global search/local search分类
信息技术与安全科学引用本文复制引用
林棋,张宏,李千目..一种基于MA-LSSVM的封装式特征选择算法[J].南京理工大学学报（自然科学版）,2016,40(1):10-16,7.基金项目
国家自然科学基金(61272419) （61272419）
江苏省未来网络前瞻性研究项目(BY2013095-3-02) （BY2013095-3-02）
江苏省产学研前瞻性项目(BY2014089) （BY2014089）
