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.

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Notes

  1. 1.

    http://archive.ics.uci.edu/ml/.

  2. 2.

    http://dblp.uni-trier.de/db/index.html.

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Acknowledgements

The work of the first author was supported in part in part by the Healthy Aging Research Center, Chang Gung University from the Featured Areas Research Center Program within the Framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan under Grants EMRPD1I0481 and EMRPD1I0501, and in part by Chang Gung Memorial Hospital, Linkou under Grant CMRPD3I0031. This research of the second author was supported by the Ministry of Science and Technology of Taiwan (MOST 105-2410-H-008-043-MY3).

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Lin, W., Tsai, C. Missing value imputation: a review and analysis of the literature (2006–2017). Artif Intell Rev 53, 1487–1509 (2020). https://doi.org/10.1007/s10462-019-09709-4

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Keywords

  • Missing values
  • Imputation
  • Supervised learning
  • Incomplete dataset
  • Data mining