An Experimental Survey of Missing Data Imputation Algorithms (Extended Abstract)

Due to the ubiquity of missing data, data imputation has received extensive attention in the past decades. It is a well-recognized problem impacting almost all fields of scientific study. Existing imputation algorithms differ in problem settings, model selection, and data evaluation. There is a lack...

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Bibliographic Details
Published in2024 IEEE 40th International Conference on Data Engineering (ICDE) pp. 5737 - 5738
Main Authors Miao, Xiaoye, Wu, Yangyang, Chen, Lu, Gao, Yunjun, Yin, Jianwei
Format Conference Proceeding
LanguageEnglish
Published IEEE 13.05.2024
Subjects
Benchmark testing
Data engineering
Data imputation
Data models
deep learning
experimental survey
Imputation
missing data
missing mecha-nism
Prediction algorithms
Surveys
Systematics
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Summary:Due to the ubiquity of missing data, data imputation has received extensive attention in the past decades. It is a well-recognized problem impacting almost all fields of scientific study. Existing imputation algorithms differ in problem settings, model selection, and data evaluation. There is a lack of systematic comparison study among imputation algorithms. In this paper, we survey this interesting and evolving research topic by broadly reviewing and experimentally comparing the state-of-the-art missing data imputation algorithms. We analyze and categorize 19 imputation algorithms. Extensive experiments over 15 real-world benchmark datasets are conducted under various settings of data types, missing mechanisms, missing rates, dataset parameters, as well as the post-imputation prediction task. We shed light on a series of constructive insights on imputation algorithms to tackle missing data problem in real-life scenarios. Moreover, we put forward promising future directions for data imputation.
ISSN:2375-026X
DOI:10.1109/ICDE60146.2024.00500