Constraint-Aware Machine Learning for Ensuring Feasible Predictions in Operational Data Science - Wu Shukun; Tri Basuki Kurniawan; Muhammet Esad Kuloğlu (International Journal of Advances in Artificial Intelligence and Machine Learning, 2026)
Background: Machine learning models deployed in operational environments often demonstrate high predictive accuracy during benchmark evaluation. However, their practical reliability is frequently compromised when predictions violate domain-specific operational constraints.Aims: This study aims to address the problem of infeasible predictions by proposing a unified framework that integrates operational constraints directly into the learning and inference processes.Methods: The CALF framework incorporates operational constraints through a dual mechanism consisting of correction-based learning and regularization-based penalty functions. These mechanisms are embedded directly within the training and inference objectives, allowing the model to learn constraint-compliant predictions during optimization. The framework was evaluated by comparing predictive error and operational feasibility against an unconstrained baseline model. Sensitivity analysis was also conducted to examine the stability and flexibility of the constraint penalties under varying operational thresholds.Result: Experimental results demonstrate that CALF achieved predictive error levels comparable to the unconstrained baseline while maintaining full operational feasibility. The framework reached 100% operational compliance, indicating that all generated predictions satisfied the defined constraints. Sensitivity analysis further showed that the regularization penalties operated within acceptable thresholds, allowing the model to maintain predictive flexibility while enforcing constraint adherence.Conclusion: The findings highlight the importance of integrating operational constraints directly into machine learning model design. By embedding feasibility constraints within the optimization process, the CALF framework ensures that predictive outputs remain both accurate and operationally compliant. This approach repositions operational constraints as intrinsic components of predictive modeling and contributes to the development of reliable and deployable AI systems in real-world environments.