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All Journal International Journal of Advances in Artificial Intelligence and Machine Learning
Wu Shukun
Guangxi Science & Technology Normal University
Computer Science & IT

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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 Vol. 3 No. 1 (2026): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v3i1.652

Abstract

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.
Learning from Heterogeneous Label Quality: Strategies for Robust Model Training Wu Shukun; Hafiz Muhammad Kurniawan; Adolf Asih Suprianto
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 3 No. 2 (2026): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v3i2.799

Abstract

Background: Training data collected from crowdsourcing platforms, automated labeling systems, and multiple annotators often contain labels with unequal levels of reliability. Conventional machine-learning methods generally treat all labels as equally trustworthy, which can reduce model performance when label errors vary across samples and annotation sources.Aims: This study proposes a quality-aware training method that explicitly models the heterogeneity of label reliability. It examines whether sample-level quality weighting can improve predictive performance and robustness under synthetic and real-world label-noise conditions.Methods: The proposed method combines annotation or neighborhood agreement, loss-based confidence, and source-level reliability to estimate a continuous quality score for each training sample. The scores are incorporated into a weighted cross-entropy objective and periodically updated during training. Experiments were conducted on CIFAR-10, CIFAR-100, CIFAR-10N, and CIFAR-100N using ResNet-18. The method was compared with cross-entropy, label smoothing, generalized cross-entropy, Co-teaching, and DivideMix.Result: Under high heterogeneous noise, the proposed method achieved accuracies of 83.21% on CIFAR-10 and 54.62% on CIFAR-100, compared with 73.48% and 45.92% for conventional cross-entropy. It also achieved the highest accuracy under the evaluated real-world noisy-label conditions. Ablation and sensitivity analyses showed that the combined quality indicators were complementary and that the method remained effective under moderate quality-estimation errors.Conclusion: Explicitly modeling label reliability improves robustness when training data contain unequal annotation quality. The proposed method provides a practical and computationally efficient strategy for learning from crowdsourced, weakly supervised, and multi-source datasets.
Co-Authors Adolf Asih Suprianto Hafiz Muhammad Kurniawan Muhammet Esad Kuloğlu Tri Basuki Kurniawan