# Impact of Missing Values in Machine Learning: A Comprehensive Analysis

arXiv: https://arxiv.org/abs/2410.08295 · HTML: https://mlsanity.com/paper/2410.08295/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 45 references that are in the map.
- First posted on arXiv 10 Oct 2024.

## Builds on (most cited first, 9 of 9)

- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Recurrent Neural Networks for Multivariate Time Series with Missing Values](https://mlsanity.com/paper/1606.01865/) (2016)
- [BRITS: Bidirectional Recurrent Imputation for Time Series](https://mlsanity.com/paper/1805.10572/) (2018)
- [SAITS: Self-Attention-based Imputation for Time Series](https://mlsanity.com/paper/2202.08516/) (2022)
- [What's a good imputation to predict with missing values?](https://mlsanity.com/paper/2106.00311/) (2021)
- [A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security](https://mlsanity.com/paper/1807.11023/) (2018)
- [Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values](https://mlsanity.com/paper/2109.10431/) (2021)
- [Adaptive imputation of missing values for incomplete pattern classification](https://mlsanity.com/paper/1602.02617/) (2016)
- [Nearest Neighbor Imputation for Categorical Data by Weighting of Attributes](https://mlsanity.com/paper/1710.01011/) (2017)

## Built on by (most cited first, 2 of 2)

- [nCMD: Benign-Anchored Feature Selection for Imbalanced Network Intrusion Detection](https://mlsanity.com/paper/2606.09934/) (2026)
- [BARS: Benign-Anchored Ranking and Selection for False Alarm Reduction in Network Intrusion Detection](https://mlsanity.com/paper/2607.13203/) (2026)

## Abstract (arXiv)

Machine learning (ML) has become a ubiquitous tool across various domains of data mining and big data analysis. The efficacy of ML models depends heavily on high-quality datasets, which are often complicated by the presence of missing values. Consequently, the performance and generalization of ML models are at risk in the face of such datasets. This paper aims to examine the nuanced impact of missing values on ML workflows, including their types, causes, and consequences. Our analysis focuses on the challenges posed by missing values, including biased inferences, reduced predictive power, and increased computational burdens. The paper further explores strategies for handling missing values, including imputation techniques and removal strategies, and investigates how missing values affect model evaluation metrics and introduces complexities in cross-validation and model selection. The study employs case studies and real-world examples to illustrate the practical implications of addressing missing values. Finally, the discussion extends to future research directions, emphasizing the need for handling missing values ethically and transparently. The primary goal of this paper is to provide insights into the pervasive impact of missing values on ML models and guide practitioners toward effective strategies for achieving robust and reliable model outcomes.