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

Ertuğrul Adıgüzel; Nadir Subaşı; Aysel Ersoy

doi:10.47000/tjmcs.1724786

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

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.

Keywords

Thanks

We would like to thank Kaancan Kırkağaç for sharing his experimental results with us for enabling their evaluation using artificial intelligence models.

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Details

Primary Language

English

Subjects

Satisfiability and Optimisation, Modelling and Simulation

Journal Section

Research Article

Authors

Ertuğrul Adıgüzel ^*
0000-0003-0687-2267
Türkiye

Nadir Subaşı
0000-0002-5657-9002
Türkiye

Aysel Ersoy
0000-0003-1164-7187
Türkiye

Publication Date

December 30, 2025

Submission Date

June 22, 2025

Acceptance Date

August 7, 2025

Published in Issue

Year 2025 Volume: 17 Number: 2

DOI

https://doi.org/10.47000/tjmcs.1724786

IZ

https://izlik.org/JA28CU89EW

Cite

RIS / Bibtex

APA

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, 17(2), 408-417. https://doi.org/10.47000/tjmcs.1724786

AMA

1.Adıgüzel E, Subaşı N, Ersoy A. Reliability Analysis of Surface Tracking Method Using Classification Methods (svm, random forest, XGBoost) with Whale Optimization Algorithm. TJMCS. 2025;17(2):408-417. doi:10.47000/tjmcs.1724786

Chicago

Adıgüzel, Ertuğrul, Nadir Subaşı, and Aysel Ersoy. 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 17 (2): 408-17. https://doi.org/10.47000/tjmcs.1724786.

EndNote

Adıgüzel E, Subaşı N, Ersoy A (December 1, 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 17 2 408–417.

IEEE

[1]E. Adıgüzel, N. Subaşı, and A. Ersoy, “Reliability Analysis of Surface Tracking Method Using Classification Methods (svm, random forest, XGBoost) with Whale Optimization Algorithm”, TJMCS, vol. 17, no. 2, pp. 408–417, Dec. 2025, doi: 10.47000/tjmcs.1724786.

ISNAD

Adıgüzel, Ertuğrul - Subaşı, Nadir - Ersoy, Aysel. “Reliability Analysis of Surface Tracking Method Using Classification Methods (svm, Random Forest, XGBoost) With Whale Optimization Algorithm”. Turkish Journal of Mathematics and Computer Science 17/2 (December 1, 2025): 408-417. https://doi.org/10.47000/tjmcs.1724786.

JAMA

1.Adıgüzel E, Subaşı N, Ersoy A. Reliability Analysis of Surface Tracking Method Using Classification Methods (svm, random forest, XGBoost) with Whale Optimization Algorithm. TJMCS. 2025;17:408–417.

MLA

Adıgüzel, Ertuğrul, et al. “Reliability Analysis of Surface Tracking Method Using Classification Methods (svm, Random Forest, XGBoost) With Whale Optimization Algorithm”. Turkish Journal of Mathematics and Computer Science, vol. 17, no. 2, Dec. 2025, pp. 408-17, doi:10.47000/tjmcs.1724786.

Vancouver

1.Ertuğrul Adıgüzel, Nadir Subaşı, Aysel Ersoy. Reliability Analysis of Surface Tracking Method Using Classification Methods (svm, random forest, XGBoost) with Whale Optimization Algorithm. TJMCS. 2025 Dec. 1;17(2):408-17. doi:10.47000/tjmcs.1724786