Cited by 6,156Statistical Methods and Bayesian InferenceStatistical Methods and InferenceGene expression and cancer classification

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MissForest—non-parametric missing value imputation for mixed-type data

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Stekhoven, Daniel J., Bühlmann, Peter·2012·Bioinformatics

DOI 10.1093/bioinformatics/btr597

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Abstract

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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Authors: Stekhoven, D. J., & Bühlmann, P.
Affiliations: ETH Zurich (CH)
Corresponding author: Stekhoven, Daniel J.
ORCID: Stekhoven, Daniel J.
Bühlmann, Peter
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
DOI: 10.1093/bioinformatics/btr597
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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MissForest—non-parametric missing value imputation for mixed-type data

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