# Nearest Neighbor Imputation for Categorical Data by Weighting of Attributes

arXiv: https://arxiv.org/abs/1710.01011 · HTML: https://mlsanity.com/paper/1710.01011/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2017.

## Built on by (most cited first, 3 of 3)

- [Impact of Missing Values in Machine Learning: A Comprehensive Analysis](https://mlsanity.com/paper/2410.08295/) (2024)
- [Missingness Augmentation: A General Approach for Improving Generative Imputation Models](https://mlsanity.com/paper/2108.02566/) (2021)
- [An Interdisciplinary and Cross-Task Review on Missing Data Imputation](https://mlsanity.com/paper/2511.01196/) (2025)

## Abstract (arXiv)

Missing values are a common phenomenon in all areas of applied research. While various imputation methods are available for metrically scaled variables, methods for categorical data are scarce. An imputation method that has been shown to work well for high dimensional metrically scaled variables is the imputation by nearest neighbor methods. In this paper, we extend the weighted nearest neighbors approach to impute missing values in categorical variables. The proposed method, called mathttwNNSelcat, explicitly uses the information on association among attributes. The performance of different imputation methods is compared in terms of the proportion of falsely imputed values. Simulation results show that the weighting of attributes yields smaller imputation errors than existing approaches. A variety of real data sets is used to support the results obtained by simulations.