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MissForest—non-parametric missing value imputation for mixed-type data
Open accessAbstract
MOTIVATION: Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis of such large-scale data often depend on a complete set. Missing value imputation offers a solution to this problem. However, the majority of available imputation methods are restricted to one type of variable only: continuous or categorical. For mixed-type data, the different types are usually handled separately. Therefore, these methods ignore possible relations between variable types. We propose a non-parametric method which can cope with different types of variables simultaneously. RESULTS: We compare several state of the art methods for the imputation of missing values. We propose and evaluate an iterative imputation method (missForest) based on a random forest. By averaging over many unpruned classification or regression trees, random forest intrinsically constitutes a multiple imputation scheme. Using the built-in out-of-bag error estimates of random forest, we are able to estimate the imputation error without the need of a test set. Evaluation is performed on multiple datasets coming from a diverse selection of biological fields with artificially introduced missing values ranging from 10% to 30%. We show that missForest can successfully handle missing values, particularly in datasets including different types of variables. In our comparative study, missForest outperforms other methods of imputation especially in data settings where complex interactions and non-linear relations are suspected. The out-of-bag imputation error estimates of missForest prove to be adequate in all settings. Additionally, missForest exhibits attractive computational efficiency and can cope with high-dimensional data. AVAILABILITY: The package missForest is freely available from http://stat.ethz.ch/CRAN/. CONTACT: stekhoven@stat.math.ethz.ch; buhlmann@stat.math.ethz.ch
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“Depending on the cause, the failure must be addressed in different ways, such as manual recalibration or data imputation [71,72].”
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Metadata
- Authors
- Stekhoven, D. J., & Bühlmann, P.
- Affiliations
- ETH Zurich (CH)
- Corresponding author
- Stekhoven, Daniel J.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2011-10-28
- Volume
- 28
- Issue
- 1
- Pages
- 112-118
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2064186732
- Cited by
- 6,156
- References
- 27
- Citations by year
- 2026: 901 · 2025: 1098 · 2024: 933 · 2023: 794 · 2022: 634 · 2021: 614 · 2020: 397 · 2019: 250 · 2018: 212 · 2017: 114 · 2016: 86 · 2015: 51 · 2014: 39 · 2013: 18 · 2012: 10
- FWCI
- 19.41
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, other-oa)
- arXiv (Cornell University)
- ETH Zürich Research Collection
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- missing data imputation, mixed data, missForest, random forest, multiple imputation, high-throughput data, high-dimensional data, biological data
- MeSH terms
- Algorithms, Data Interpretation, Statistical, Escherichia coli, Humans, Arabidopsis, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling
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