Missing value imputation: a review and analysis of the literature (2006–2017)

被引：6
作者：
Wei-Chao Lin
Chih-Fong Tsai
机构：
[1] Chang Gung University,Department of Information Management
[2] Chang Gung University,Healthy Aging Research Center
[3] Chang Gung Memorial Hospital,Department of Thoracic Surgery
[4] National Central University,Department of Information Management
来源：
Artificial Intelligence Review | 2020年 / 53卷
关键词：
Missing values; Imputation; Supervised learning; Incomplete dataset; Data mining;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Missing value imputation (MVI) has been studied for several decades being the basic solution method for incomplete dataset problems, specifically those where some data samples contain one or more missing attribute values. This paper aims at reviewing and analyzing related studies carried out in recent decades, from the experimental design perspective. Altogether, 111 journal papers published from 2006 to 2017 are reviewed and analyzed. In addition, several technical issues encountered during the MVI process are addressed, such as the choice of datasets, missing rates and missingness mechanisms, and the MVI techniques and evaluation metrics employed, are discussed. The results of analysis of these issues allow limitations in the existing body of literature to be identified based upon which some directions for future research can be gleaned.
引用
收藏
页码：1487 / 1509
页数：22
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