Journal
ARTIFICIAL INTELLIGENCE REVIEW
Volume 53, Issue 2, Pages 1487-1509Publisher
SPRINGER
DOI: 10.1007/s10462-019-09709-4
Keywords
Missing values; Imputation; Supervised learning; Incomplete dataset; Data mining
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Funding
- Healthy Aging Research Center, Chang Gung University from the Featured Areas Research Center Program within Ministry of Education (MOE) in Taiwan [EMRPD1I0481, EMRPD1I0501]
- Chang Gung Memorial Hospital, Linkou [CMRPD3I0031]
- Ministry of Science and Technology of Taiwan [MOST 105-2410-H-008-043-MY3]
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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.
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