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.
Rezaei Shiri, B., & Eftekhari Mahabadi, S. (2021). Missing data imputation using supervised learning methods. Journal of Statistical Modelling: Theory and Applications, 2(2), 103-112. doi: 10.22034/jsmta.2021.2049
MLA
Behzad Rezaei Shiri; Samaneh Eftekhari Mahabadi. "Missing data imputation using supervised learning methods", Journal of Statistical Modelling: Theory and Applications, 2, 2, 2021, 103-112. doi: 10.22034/jsmta.2021.2049
HARVARD
Rezaei Shiri, B., Eftekhari Mahabadi, S. (2021). 'Missing data imputation using supervised learning methods', Journal of Statistical Modelling: Theory and Applications, 2(2), pp. 103-112. doi: 10.22034/jsmta.2021.2049
VANCOUVER
Rezaei Shiri, B., Eftekhari Mahabadi, S. Missing data imputation using supervised learning methods. Journal of Statistical Modelling: Theory and Applications, 2021; 2(2): 103-112. doi: 10.22034/jsmta.2021.2049
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