Evaluating Data-Centric Optimization Strategies for Improving Machine Learning Generalization

Abstract

Machine learning research has traditionally emphasized model-centric optimization, often overlooking the critical role of data quality in determining generalization performance. However, real-world datasets frequently suffer from noise, imbalance, and limited diversity, which constrain model effectiveness despite increasing architectural complexity. Existing studies typically evaluate data preprocessing techniques in isolation and lack a unified framework to assess their combined impact. To address this gap, this study proposes a comprehensive data-centric optimization framework that systematically integrates data cleaning, rebalancing, and targeted augmentation to enhance dataset quality. Multiple benchmark datasets with diverse characteristics were utilized, and models were trained under controlled experimental settings to isolate the effects of data refinement strategies. The results demonstrate that data-centric interventions consistently outperform model-centric improvements, with the proposed approach achieving up to 95% accuracy, significantly surpassing baseline models trained on raw data. Notably, simpler models trained on refined datasets achieve competitive or superior performance compared to more complex models, highlighting the efficiency of data-centric optimization. Statistical analysis confirms that the improvements are significant and robust across different datasets and conditions. This study aims to quantify the impact of data quality on machine learning generalization and provide empirical evidence supporting a paradigm shift toward data-centric AI. The findings offer practical guidelines for prioritizing data refinement in machine learning pipelines and contribute to the development of more robust, scalable, and efficient intelligent systems.