Benchmarking Classification Performance for Binary-Class Fault Detection Under Real-World Imbalanced Data Conditions

Bagai Glory Kuzayet, Eje Obed Honour, Jibril Abdullahi Bala
Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments, PMLR 319:278-292, 2026.

Abstract

This study addresses predictive maintenance in Nigeria’s manufacturing sector by benchmarking Logistic Regression, Random Forest, SVM, and XGBoost on 7,672 real-time sensor measurements from rotating electromechanical machinery. Beyond standard benchmarking, we propose a stacking ensemble combining SVM, Random Forest, and XGBoost as heterogeneous base learners under a Logistic Regression meta-learner. The stacked ensemble achieves an accuracy of 98.37% and an F1-score of 91.35%, establishing an empirical foundation for intelligent, data-driven operations aligned with Industry 4.0 principles in Nigerian industrial settings.

Cite this Paper

BibTeX
@InProceedings{pmlr-v319-kuzayet26a, title = {Benchmarking Classification Performance for Binary-Class Fault Detection Under Real-World Imbalanced Data Conditions}, author = {Kuzayet, Bagai Glory and Honour, Eje Obed and Bala, Jibril Abdullahi}, booktitle = {Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments}, pages = {278--292}, year = {2026}, editor = {Folorunso, Sakinat and Ogundokun, Roseline and Oladipo, Francisca}, volume = {319}, series = {Proceedings of Machine Learning Research}, month = {11--14 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v319/main/assets/kuzayet26a/kuzayet26a.pdf}, url = {https://proceedings.mlr.press/v319/kuzayet26a.html}, abstract = {This study addresses predictive maintenance in Nigeria’s manufacturing sector by benchmarking Logistic Regression, Random Forest, SVM, and XGBoost on 7,672 real-time sensor measurements from rotating electromechanical machinery. Beyond standard benchmarking, we propose a stacking ensemble combining SVM, Random Forest, and XGBoost as heterogeneous base learners under a Logistic Regression meta-learner. The stacked ensemble achieves an accuracy of 98.37% and an F1-score of 91.35%, establishing an empirical foundation for intelligent, data-driven operations aligned with Industry 4.0 principles in Nigerian industrial settings.} }
Endnote
%0 Conference Paper %T Benchmarking Classification Performance for Binary-Class Fault Detection Under Real-World Imbalanced Data Conditions %A Bagai Glory Kuzayet %A Eje Obed Honour %A Jibril Abdullahi Bala %B Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments %C Proceedings of Machine Learning Research %D 2026 %E Sakinat Folorunso %E Roseline Ogundokun %E Francisca Oladipo %F pmlr-v319-kuzayet26a %I PMLR %P 278--292 %U https://proceedings.mlr.press/v319/kuzayet26a.html %V 319 %X This study addresses predictive maintenance in Nigeria’s manufacturing sector by benchmarking Logistic Regression, Random Forest, SVM, and XGBoost on 7,672 real-time sensor measurements from rotating electromechanical machinery. Beyond standard benchmarking, we propose a stacking ensemble combining SVM, Random Forest, and XGBoost as heterogeneous base learners under a Logistic Regression meta-learner. The stacked ensemble achieves an accuracy of 98.37% and an F1-score of 91.35%, establishing an empirical foundation for intelligent, data-driven operations aligned with Industry 4.0 principles in Nigerian industrial settings.
APA
Kuzayet, B.G., Honour, E.O. & Bala, J.A.. (2026). Benchmarking Classification Performance for Binary-Class Fault Detection Under Real-World Imbalanced Data Conditions. Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments, in Proceedings of Machine Learning Research 319:278-292 Available from https://proceedings.mlr.press/v319/kuzayet26a.html.

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