Tabular and graph-based representations for noise and missing data in robust machine learning

The performance of machine learning models in industrial settings is often limited by noise and missing values in real-world data. Tabular data representations, commonly used in traditional machine learning, may not effectively capture complex relationships or maintain reliability under such data degradation. This study comparatively evaluates the robustness of tabular and graph-based data representations for machine learning models when faced with data corruption. Using a real-world steel industry energy consumption dataset, we assess six models: Random Forest, XGBoost, Multi-Layer Perceptron (MLP), Graph Convolutional Network, SAGE, and Graph Attention Network, across clean, noisy, missing, and combined noise and missing data scenarios. A novel transformation technique converts tabular data into graph structures to facilitate relational learning in graph-based models. Graph-based models demonstrated 30.8% greater robustness than tabular models, as measured by their lower average drop in classification accuracy across missing, noisy, and combined data corruption scenarios. These findings pave the way for deploying more resilient artificial intelligence (AI) systems in complex industrial environments, emphasizing the critical role of relational data representations in robust machine learning. For validation, we applied another study with the UCI Machine Learning Repository: the Concrete Compressive Strength Dataset, and found comparable resonance in this regard.