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

• Published in: The Artificial intelligence review Vol. 53; no. 2; pp. 1487 - 1509
• Main Authors: Lin, Wei-Chao, Tsai, Chih-Fong
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.02.2020; Springer; Springer Nature B.V
• Subjects: Artificial Intelligence; Big Data; Computer Science; Data analysis; Data mining; Datasets; Design of experiments; Information management; Intelligent systems; Machine learning; Medical research; Missing data; Performance evaluation; Thoracic surgery; Imputation; Data mining; Supervised learning; Incomplete dataset; Missing values
• ISSN: 0269-2821; 1573-7462
• DOI: 10.1007/s10462-019-09709-4

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.