Paper Info
Title
Missing value imputation: a review and analysis of the literature (2006–2017)
Abstract
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.
Year
DOI
Venue
2020
10.1007/s10462-019-09709-4
Artificial Intelligence Review
Keywords
Field
DocType
Missing values, Imputation, Supervised learning, Incomplete dataset, Data mining
Data mining,Computer science,Supervised learning,Artificial intelligence,Imputation (statistics),Missing data,Missing value imputation,Machine learning
Journal
Volume
Issue
ISSN
53
2
0269-2821
Citations 
PageRank 
References 
6
0.70
0
Authors
3
Name
Order
Citations
PageRank
Wei-Chao Lin1121.40
Wei-Chao Lin2121.40
Chih-fong Tsai3125554.93