计算机工程与应用2024，Vol.60Issue(8)：121-130,10.DOI:10.3778/j.issn.1002-8331.2212-0094
融合Lasso的近似马尔科夫毯特征选择方法
Approximate Markov Blanket Feature Selection Method Based on Lasso Fusion
摘要
Abstract
In feature selection,approximate Markov blankets are often used to judge redundant features,but the redun-dant features obtained are not identical.Therefore,when using approximate Markov blankets directly to delete redundant features,there may be situations that may lead to information loss and affect model accuracy.Therefore,an approximate Markov blanket feature selection method based on Lasso fusion for high-dimensional small sample data of traditional Chinese medicine metabonomics is proposed.The method is divided into two stages.In the first stage,irrelevant features are filtered by analyzing the correlation of features with the maximum information coefficient.In the second stage,approximate Markov blankets are used to construct similar feature groups,Lasso is used to evaluate the influence of features in similar feature groups,and redundant features are removed iteratively.The experimental results show that the algorithm can reduce the loss of useful information,remove irrelevant features and redundant features,and improve the accuracy and stability of the model.关键词
近似马尔科夫毯/Lasso/特征选择/高维小样本/中医药信息Key words
approximate Markov blanke/Lasso/feature selection/high dimensional small sample/traditional Chinese medicine(TCM)information分类
信息技术与安全科学引用本文复制引用
刘明,杜建强,李郅琴,罗计根,聂斌,张梦婷..融合Lasso的近似马尔科夫毯特征选择方法[J].计算机工程与应用,2024,60(8):121-130,10.基金项目
国家自然科学基金(62141202,82160955,82260988) （62141202,82160955,82260988）
国家重点研发计划项目(2019YFC1712301) （2019YFC1712301）
江西省自然科学基金面上项目(20202BAB202019) （20202BAB202019）
江西省教育厅科学技术研究项目(GJJ190683) （GJJ190683）
江西中医药大学校级科技创新团队发展计划(CXTD22015). （CXTD22015）
