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Journals / Turkish Journal of Mathematics and Computer Science / 2025 / Volume: 17 Issue: 2

Reliability Analysis of Surface Tracking Method Using Classification Methods (svm, random forest, XGBoost) with Whale Optimization Algorithm

Ertuğrul Adıgüzel(İSTANBUL ÜNİVERSİTESİ-CERRAHPAŞA)0000-0003-0687-2267
Nadir Subaşı(KIRIKKALE ÜNİVERSİTESİ, KIRIKKALE MESLEK YÜKSEKOKULU)0000-0002-5657-9002
Aysel Ersoy(ISTANBUL UNIVERSITY-CERRAHPASA)0000-0003-1164-7187
Pages
—
Publication Date
2025-12-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

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Objective & Hypothesis: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).
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Key Findings & Data: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.
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Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:180 samples
⚙️Variables:Surface tracking · Reliability analysis · Whale Optimization Algorithm (WOA)

Abstract

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.

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Cite This Article

Adıgüzel, E., Subaşı, N., Ersoy, A. (2025). Reliability Analysis of Surface Tracking Method Using Classification Methods (svm, random forest, XGBoost) with Whale Optimization Algorithm. Turkish Journal of Mathematics and Computer Science, Volume: 17 Issue: 2. https://doi.org/10.47000/tjmcs.1724786

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