A Perspective of Missing Value Imputation Approaches

  • Wajeeha Rashid
  • Manoj Kumar GuptaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1086)


A massive amount of data is emerging continuously in the era of big data; the missing value is a common yet challenging problem. Missing data or missing value can be described as the data whose values in any given dataset are unknown. It is a recurrent phenomenon and can give biased results from the data to be observed and affect the value of the learning process. Therefore, it is mandatory to use missing value imputation techniques. Missing value imputation provides (MVI) optimal solutions for missing values in a dataset. There are diverse missing value imputation techniques such as statistical method and machine learning methods proposed till date, each having their own significances and flaws. Reasonably, good imputation results may be produced by machine learning MVI approaches but they take greater imputation times than statistical approaches usually. In this paper, we have reviewed some of the missing value imputation approaches proposed. In order to show the efficiency of their proposed approaches, they had compared their proposed methods with some baseline imputation algorithms like mode/median, k-nearest neighbor, Naive Bayes, support vector machine. The outcome of each proposed method is analyzed and their extension scopes from the perspectives of research focus. The main idea of this literature is to give a general review of missing value imputation approaches.


Big data Missing data Missing data imputation Data mining Machine learning MCAR MAR MNAR 


  1. 1.
    X. Xu et al., MIAEC: Missing data imputation based on the evidence chain. IEEE Access (2018)Google Scholar
  2. 2.
    J. Han, M. Kamber, Data Mining Concept and Techniques, 3rd edn. (Multiscience Press, USA), pp. 226 (2012)Google Scholar
  3. 3.
    P. Keerin, W. Kurutach, T. Boongoen, Cluster-based KNN missing value imputation for DNA microarray data, in 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Seoul (2012), pp. 445–450Google Scholar
  4. 4.
    X. Zhu et al., Missing value estimation for mixed-attribute data sets. IEEE Trans. Knowl. Data Eng. 23(1), 110–121 (2011)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Y.S. Qin et al., POP algorithm: kernel-based imputation to treat missing values in knowledge discovery from databaseGoogle Scholar
  6. 6.
    S.C. Zhang, Parimputation: from imputation and null-imputation to partially imputation. IEEE Intell. Inform. Bull. 9(1), 32–38 (2008)MathSciNetGoogle Scholar
  7. 7.
    J. Racine, Q. Li, Nonparametric estimation of regression functions with both categorical and continuous data. J. Econometrics 119(1), 99–130 (2004)MathSciNetCrossRefGoogle Scholar
  8. 8.
    C.-F. Tsai, M.-L. Li, W.-C. Lin, A class center-based approach for missing value imputation. Knowl. Based Syst. 151, 124–135 (2018)CrossRefGoogle Scholar
  9. 9.
    R. Pan, T. Yang, J. Cao, K. Lu, Z. Zhang, Missing data imputation by K nearest neighbors based on the grey relational structure and mutual information. Appl. Intell. 43, 614–632 (2015)CrossRefGoogle Scholar
  10. 10.
    K. Pelckmans, J.D. Brabanter, J.A.K. Suykens, B.D. Moor, Handling missing values in support vector machine classifiers. Neural Netw. 18, 684–692 (2005)CrossRefGoogle Scholar
  11. 11.
    M.G. Rahman, M.Z. Islam, Missing value imputation using decision trees and decision forests by splitting and merging records: two novel techniques. Knowl. Based Syst. 53, 51–65 (2013)CrossRefGoogle Scholar
  12. 12.
    M.G. Rahman, M.Z. Islam, iDMI: a novel technique for missing value imputation using a decision tree and expectation-maximization algorithm, in 2013 16th International Conference on Computer and Information Technology (ICCIT), Khulna (2014), pp. 496–501Google Scholar
  13. 13.
    T. Schneider, Analysis of incomplete climate data: estimation of mean values and covariance matrices and imputation of missing values. J. Clim. 14(5), 853–871 (2001)CrossRefGoogle Scholar
  14. 14.
    K. Cheng, N. Law, W. Siu, An Iterative bicluster based least square framework for estimation of missing values in microarray gene expression data. Pattern Recogn. 45(4), 1281–1289 (2012)CrossRefGoogle Scholar
  15. 15.
    C.-F. Tsai, F.-Y. Chang, Combining instance selection for better missing value imputation. J. Syst. Softw. 122, 63–71 (2016)CrossRefGoogle Scholar
  16. 16.
    M. Amiri, R. Jensen, Missing data imputation using fuzzy-rough methods. Neurocomputing 205, 152–164 (2016)CrossRefGoogle Scholar
  17. 17.
    R. Jensen, C. Cornelis, Fuzzy-rough nearest neighbor classification, in Transactions on Rough Sets XIII (Springer, Berlin, 2011), pp. 56–72Google Scholar
  18. 18.
    Y. Qin et al., Semi-parametric optimization for missing data imputation. Appl. Intell. 27(1), 79–88 (2007)CrossRefGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringShri Mata Vaishno Devi UniversityKatraIndia

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