Comparison of Imputation Methods Based on Missing Value Detection for Multidimensional Feature Data

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.