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