Missing data imputation using supervised learning methods

Document Type : Original Scientific Paper

Authors

School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran

Abstract

Missing data is a very common problem in all research fields. Case deletion is a simple way to handle incomplete data sets which could mislead to biased statistical results. A more reliable approach to handle missing values is imputation which allows covariate-dependent missing mechanism, as well. This paper aims to prepare guidance for researchers facing missing data problems by comparing various imputation methods including machine learning techniques, to achieve better results in supervised learning tasks. A benchmark dataset has experimented and the results are compared by applying popular classifiers over varying missing mechanisms and rates on this benchmark dataset.

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References

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Journal of Statistical Modelling: Theory and Applications
Volume 2, Issue 2
July 2021
Pages 103-112

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History

  • Receive Date: 30 September 2020
  • Revise Date: 26 February 2021
  • Accept Date: 20 April 2021

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