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Tlamelo Emmanuel, Thabiso Maupong, Dimane Mpoeleng, Thabo Semong, Banyatsang Mphago, Oteng Tabona
Machine learning has been the corner stone in analysing and extracting information from data and often a problem of missing values is encountered. Missing values occur because of various factors like missing completely at random, missing at random or missing not at random. All these may result from system malfunction during data collection or human error during data pre-processing. Nevertheless, it is important to deal with missing values before analysing data since ignoring or omitting missing values may result in biased or misinformed analysis. In literature there have been several proposals for handling missing values. In this paper, we aggregate some of the literature on missing data particularly focusing on machine learning techniques. We also give insight on how the machine learning approaches work by highlighting the key features of missing values imputation techniques, how they perform, their limitations and the kind of data they are most suitable for. We propose and evaluate two methods, the k nearest neighbor and an iterative imputation method (missForest) based on the random forest algorithm. Evaluation is performed on the Iris and novel power plant fan data with induced missing values at missingness rate of 5% to 20%. We show that both missForest and the k nearest neighbor can successfully handle missing values and offer some possible future research direction.
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Emmanuel, T., Maupong, T., Mpoeleng, D., Semong, T., Mphago, B., & Tabona, O. (2021). A survey on missing data in machine learning. Journal Of Big Data, 8(1), 140. https://doi.org/10.1186/s40537-021-00516-9
Emmanuel, Tlamelo, et al. "A survey on missing data in machine learning." Journal Of Big Data, vol. 8, no. 1, 2021, pp. 140, https://doi.org/10.1186/s40537-021-00516-9.
Emmanuel, Tlamelo, Thabiso Maupong, Dimane Mpoeleng, Thabo Semong, Banyatsang Mphago, and Oteng Tabona. "A survey on missing data in machine learning." Journal Of Big Data 8, no. 1 (2021): 140. https://doi.org/10.1186/s40537-021-00516-9.
Emmanuel T., Maupong T., Mpoeleng D. [и др.] A survey on missing data in machine learning // Journal Of Big Data. — 2021. — Vol. 8, № 1. — P. 140. — URL: https://doi.org/10.1186/s40537-021-00516-9
@article{emmanuel2021survey,
title = {{A survey on missing data in machine learning}},
author = {Tlamelo Emmanuel and Thabiso Maupong and Dimane Mpoeleng and Thabo Semong and Banyatsang Mphago and Oteng Tabona},
journal = {Journal Of Big Data},
year = {2021},
volume = {8},
number = {1},
pages = {140},
doi = {10.1186/s40537-021-00516-9},
url = {https://doi.org/10.1186/s40537-021-00516-9}
}TY - JOUR AU - Emmanuel, Tlamelo AU - Maupong, Thabiso AU - Mpoeleng, Dimane AU - Semong, Thabo AU - Mphago, Banyatsang AU - Tabona, Oteng TI - A survey on missing data in machine learning JO - Journal Of Big Data VL - 8 IS - 1 SP - 140 PY - 2021 DA - 2021/10/27 DO - 10.1186/s40537-021-00516-9 UR - https://doi.org/10.1186/s40537-021-00516-9 ER -
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