Missing data imputation using supervised learning methods
Publish Year: 1400
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_JSMTA-2-2_007
تاریخ نمایه سازی: 4 تیر 1401
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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Authors
Behzad Rezaei Shiri
School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran
Samaneh Eftekhari Mahabadi
School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran