RU
Hierarchical Nearest Neighbor Search for Missing Value Imputation in Tabular Data
Received: 21 Mar 2026 Revised: 25 May 2026 Accepted: 27 May 2026
Published: 2026, vol. 30, issue 3, pp. 8–29
Abstract
Missing data represent one of the most frequent difficulties encountered in data science. A wide range of effective techniques for dealing with missing values has been developed to date. One commonly adopted approach relies on nearest neighbor methods, which perform imputation by leveraging information from the most similar observations. Such techniques are particularly prevalent in big data analysis, where datasets can comprise billions of instances. Under these conditions, improving the efficiency of \(k\)-Nearest Neighbors (KNN) algorithms becomes crucial, as their computational cost can be substantial. In this study, we investigate approaches for accelerating missing value imputation based on KNN. We introduce a novel algorithm that employs approximate nearest neighbor (ANN) techniques and examine the relationship between computational efficiency and imputation accuracy. The experimental results demonstrate that widely used ANN algorithms are capable of significantly reducing runtime while preserving a high level of imputation quality.
Keywords: missing values filling, approximate nearest neighbors.
BibTeX
@article{IS-Artamonov-Mironov2026,
author = {Artamonov, Sergei Aleksandrovich and Mironov, Andrei Mikhailovich},
title = {{Hierarchical Nearest Neighbor Search for Missing Value Imputation in Tabular Data}},
journal = {Intelligent Systems. Theory and Applications},
year = {2026},
volume = {30},
number = {3},
pages = {8--29},
}
AMSBIB
\Bibitem{IS-Artamonov-Mironov2026}
\by S.\,A.~Artamonov, A.\,M.~Mironov
\paper Hierarchical Nearest Neighbor Search for Missing Value Imputation in Tabular Data
\jour Intelligent Systems. Theory and Applications
\yr 2026
\vol 30
\issue 3
\pages 8--29
\lang In Russian