Global Journal of Engineering and Technology Research (GJETR)

Enhancing Machine Learning Robustness in Noisy and Incomplete Datasets

Christopher, Maduabuchukwu, Okeraghogho, Ekuerhare

29 April 2026 · Vol. 2, Issue 4, pp. 121-126

DOI: 10.65150/EP-gjetr/V2E4/2026-03

Abstract

Machine learning (ML) models have achieved impressive performance across diverse applications. However, their accuracy and generalizability are often compromised by real-world data irregularities such as noise, outliers, and missing values. These imperfections are common in domains like healthcare, finance and remote sensing, where acquiring clean, complete datasets is challenging. This paper adopted a hybrid methodological approach comprising both experimental and analytical frameworks and to investigate techniques that improve the robustness of ML models when faced with such noisy or incomplete datasets. The results of the UCI Adult dataset show how sensitive machine learning models are to missing data under the Missing At Random (MAR) mechanism at a corruption threshold of thirty percent. When mean imputation and XGBoost were used together, the accuracy significantly decreased from 85% to 70%, or 15 percentage points (pp). This significant deterioration shows that basic imputation methods, such as mean replacement, are unable to maintain the underlying data distribution and variable linkages. The predictive power of models is ultimately weakened by mean imputation, which tends to increase bias and minimize variance. Specifically, it will explore methods like noise-tolerant training algorithms, imputation strategies, ensemble learning, and adversarial robustness. By doing so, the study contributes to the growing field of reliable and explainable artificial intelligence, ensuring ML systems remain effective in uncertain environments. In conclusion, this study underscores the need for developing machine learning models that remain dependable in the face of noisy and incomplete datasets and recommended amongst others that future ML development workflows integrate noise-handling techniques from the early stages of data preprocessing to model deployment.

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Christopher, Maduabuchukwu, Okeraghogho, & Ekuerhare (2026). Enhancing Machine Learning Robustness in Noisy and Incomplete Datasets. Global Journal of Engineering and Technology Research, 2(4), 121-126. https://doi.org/10.65150/EP-gjetr/V2E4/2026-03

@article{Christopher2026,
  title   = {Enhancing Machine Learning Robustness in Noisy and Incomplete Datasets},
  author  = {Christopher and Maduabuchukwu and Okeraghogho and Ekuerhare},
  journal = {Global Journal of Engineering and Technology Research},
  year    = {2026},
  volume  = {2},
  number  = {4},
  pages   = {121-126},
  doi     = {10.65150/EP-gjetr/V2E4/2026-03},
  url     = {https://doi.org/10.65150/EP-gjetr/V2E4/2026-03}
}

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