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Missing values are a common problem in data science and machine learning.Removing instances with missing values can adversely affect the quality offurther data analysis. This is exacerbated when there are relatively many morefeatures than instances, and thus the proportion of affected instances is high.Such a scenario is common in many important domains, for example, singlenucleotide polymorphism (SNP) datasets provide a large number of features overa genome for a relatively small number of individuals. To preserve as muchinformation as possible prior to modeling, a rigorous imputation scheme isacutely needed. While Denoising Autoencoders is a state-of-the-art method forimputation in high-dimensional data, they still require enough complete casesto be trained on which is often not available in real-world problems. In thispaper, we consider missing value imputation as a multi-label classificationproblem and propose Chains of Autoreplicative Random Forests. Using multi-labelRandom Forests instead of neural networks works well for low-sampled data asthere are fewer parameters to optimize. Experiments on several SNP datasetsshow that our algorithm effectively imputes missing values based only oninformation from the dataset and exhibits better performance than standardalgorithms that do not require any additional information. In this paper, thealgorithm is implemented specifically for SNP data, but it can easily beadapted for other cases of missing value imputation.

Chains of Autoreplicative Random Forests for missing value imputation in high-dimensional datasets