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Outlier Removal in Model-Based Missing Value Imputation for Medical Datasets
Joint Authors
Tsai, Chih-Fong
Huang, Min-Wei
Lin, Wei-Chao
Source
Journal of Healthcare Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-02-04
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Many real-world medical datasets contain some proportion of missing (attribute) values.
In general, missing value imputation can be performed to solve this problem, which is to provide estimations for the missing values by a reasoning process based on the (complete) observed data.
However, if the observed data contain some noisy information or outliers, the estimations of the missing values may not be reliable or may even be quite different from the real values.
The aim of this paper is to examine whether a combination of instance selection from the observed data and missing value imputation offers better performance than performing missing value imputation alone.
In particular, three instance selection algorithms, DROP3, GA, and IB3, and three imputation algorithms, KNNI, MLP, and SVM, are used in order to find out the best combination.
The experimental results show that that performing instance selection can have a positive impact on missing value imputation over the numerical data type of medical datasets, and specific combinations of instance selection and imputation methods can improve the imputation results over the mixed data type of medical datasets.
However, instance selection does not have a definitely positive impact on the imputation result for categorical medical datasets.
American Psychological Association (APA)
Huang, Min-Wei& Lin, Wei-Chao& Tsai, Chih-Fong. 2018. Outlier Removal in Model-Based Missing Value Imputation for Medical Datasets. Journal of Healthcare Engineering،Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1186965
Modern Language Association (MLA)
Huang, Min-Wei…[et al.]. Outlier Removal in Model-Based Missing Value Imputation for Medical Datasets. Journal of Healthcare Engineering No. 2018 (2018), pp.1-9.
https://search.emarefa.net/detail/BIM-1186965
American Medical Association (AMA)
Huang, Min-Wei& Lin, Wei-Chao& Tsai, Chih-Fong. Outlier Removal in Model-Based Missing Value Imputation for Medical Datasets. Journal of Healthcare Engineering. 2018. Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1186965
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1186965