Enhancing Machine Learning Robustness in Noisy and Incomplete Datasets
Authors: Maduabuchukwu Christopher; Ekuerhare Okeraghogho
Journal: Global Journal of Engineering and Technology Review (GJETR)
Published: 2026-04-29 · Volume 2, Issue 04, 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,