Akademik Veri Yönetim Sistemi
Turkish Journal of Mathematics and Computer Science, cilt.17, sa.2, ss.408-417, 2025 (Scopus, TRDizin)
This study presents a comprehensive reliability analysis of a surface tracking methodology integrating
machine learning classifiers—Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient
Boosting (XGBoost)—optimized via the Whale Optimization Algorithm (WOA). The framework addresses surface
degradation detection by leveraging experimental data from epoxy/MgO nanocomposite materials subjected
to hydro-aging and electrical tracking conditions. Features representing physical and electrical behaviors were
engineered and subjected to advanced preprocessing and classification workflows. WOA was employed for hyperparameter
tuning and feature importance calibration, aiming to improve model robustness and accuracy. Results
indicate that XGBoost, when optimized with WOA, outperforms SVM and RF in all metrics, achieving 92.3% accuracy
and a macro F1-score of 0.94, with enhanced generalizability and resilience to class imbalance. The dataset
consists of 180 samples, categorized into three reliability classes (‘low’, ‘medium’, and ‘high’) based on the quartile
thresholds of reliability scores. The study underlines the critical importance of nature-inspired optimization in
classification pipelines and provides a robust blueprint for predictive modeling in materials reliability applications.