同济大学学报（自然科学版）2023，Vol.51Issue(12)：1972-1982,11.DOI:10.11908/j.issn.0253-374x.22166
面向多维特性数据的缺失值检测及填补方法对比
Comparison of Imputation Methods Based on Missing Value Detection for Multidimensional Feature Data
摘要
Abstract
Aiming at the problems that traditional missing value detection methods are not comprehensive enough to analyze the multidimensional feature data and it is difficult to select the most appropriate missing value algorithm among numerous methods,this paper first designs a missing value detection method and then proposes three different concepts of missing degree to achieve the comprehensive detection of the data with multidimensional features.On this basis,it compares and analyzes the performance of different missing value imputation methods.The results show that the proposed detection method can evaluate the data with multidimensional features effectively and provide basis for the selection of missing value imputation methods.关键词
数据预处理/缺失值检测/缺失度/缺失值填补方法Key words
data preprocessing/missing value detection/missing degree/missing value imputation methods分类
信息技术与安全科学引用本文复制引用
乔非,翟晓东,王巧玲..面向多维特性数据的缺失值检测及填补方法对比[J].同济大学学报（自然科学版）,2023,51(12):1972-1982,11.基金项目
科技创新 2030"新一代人工智能"重大项目(2018AAA0101704) （2018AAA0101704）
国家自然科学基金(62133011,61973237,61873191) （62133011,61973237,61873191）
