Analyzing the Effect of Imputation on Classification Performance under MCAR and MAR Missing Mechanisms
Many datasets in statistical analyses contain missing values. As omitting observations containing missing entries may lead to information loss or greatly reduce the sample size, imputation is usually preferable. However, imputation can also introduce bias and impact the quality and validity of subse...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/25/3/521 |
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| author | Philip Buczak Jian-Jia Chen Markus Pauly |
| author_facet | Philip Buczak Jian-Jia Chen Markus Pauly |
| author_sort | Philip Buczak |
| collection | DOAJ |
| description | Many datasets in statistical analyses contain missing values. As omitting observations containing missing entries may lead to information loss or greatly reduce the sample size, imputation is usually preferable. However, imputation can also introduce bias and impact the quality and validity of subsequent analysis. Focusing on binary classification problems, we analyzed how missing value imputation under MCAR as well as MAR missingness with different missing patterns affects the predictive performance of subsequent classification. To this end, we compared imputation methods such as several MICE variants, missForest, Hot Deck as well as mean imputation with regard to the classification performance achieved with commonly used classifiers such as Random Forest, Extreme Gradient Boosting, Support Vector Machine and regularized logistic regression. Our simulation results showed that Random Forest based imputation (i.e., MICE Random Forest and missForest) performed particularly well in most scenarios studied. In addition to these two methods, simple mean imputation also proved to be useful, especially when many features (covariates) contained missing values. |
| first_indexed | 2024-03-11T06:35:24Z |
| format | Article |
| id | doaj.art-f05fb1b972df4192acb3f24ec398effb |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-11T06:35:24Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-f05fb1b972df4192acb3f24ec398effb2023-11-17T10:57:22ZengMDPI AGEntropy1099-43002023-03-0125352110.3390/e25030521Analyzing the Effect of Imputation on Classification Performance under MCAR and MAR Missing MechanismsPhilip Buczak0Jian-Jia Chen1Markus Pauly2Department of Statistics, TU Dortmund University, 44227 Dortmund, GermanyDepartment of Computer Science, TU Dortmund University, 44227 Dortmund, GermanyDepartment of Statistics, TU Dortmund University, 44227 Dortmund, GermanyMany datasets in statistical analyses contain missing values. As omitting observations containing missing entries may lead to information loss or greatly reduce the sample size, imputation is usually preferable. However, imputation can also introduce bias and impact the quality and validity of subsequent analysis. Focusing on binary classification problems, we analyzed how missing value imputation under MCAR as well as MAR missingness with different missing patterns affects the predictive performance of subsequent classification. To this end, we compared imputation methods such as several MICE variants, missForest, Hot Deck as well as mean imputation with regard to the classification performance achieved with commonly used classifiers such as Random Forest, Extreme Gradient Boosting, Support Vector Machine and regularized logistic regression. Our simulation results showed that Random Forest based imputation (i.e., MICE Random Forest and missForest) performed particularly well in most scenarios studied. In addition to these two methods, simple mean imputation also proved to be useful, especially when many features (covariates) contained missing values.https://www.mdpi.com/1099-4300/25/3/521missing valuesimputationMICEmissForestclassificationmachine learning |
| spellingShingle | Philip Buczak Jian-Jia Chen Markus Pauly Analyzing the Effect of Imputation on Classification Performance under MCAR and MAR Missing Mechanisms Entropy missing values imputation MICE missForest classification machine learning |
| title | Analyzing the Effect of Imputation on Classification Performance under MCAR and MAR Missing Mechanisms |
| title_full | Analyzing the Effect of Imputation on Classification Performance under MCAR and MAR Missing Mechanisms |
| title_fullStr | Analyzing the Effect of Imputation on Classification Performance under MCAR and MAR Missing Mechanisms |
| title_full_unstemmed | Analyzing the Effect of Imputation on Classification Performance under MCAR and MAR Missing Mechanisms |
| title_short | Analyzing the Effect of Imputation on Classification Performance under MCAR and MAR Missing Mechanisms |
| title_sort | analyzing the effect of imputation on classification performance under mcar and mar missing mechanisms |
| topic | missing values imputation MICE missForest classification machine learning |
| url | https://www.mdpi.com/1099-4300/25/3/521 |
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