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:

针对传统缺失值检测方法缺少对多维特性数据全面立体的分析及难以从众多缺失值填补算法中选择合适方法的问题，通过设计缺失值检测方法，在目前常见的数据点缺失度基础上，首次提出数据总体缺失度和加权数据总体缺失度的概念，实现对数据集缺失程度的全面检测，进而通过实验对比分析不同缺失值填补方法性能。实验结果表明，在不同缺失度的情况下，不同缺失值填补算法的性能不同，所提出的方法可为缺失值填补算法的选择提供有效依据。

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.