Simultaneous Feature Selection and Hyperparameter Tuning of K-Nearest Neighbors for Glass Classification: A Comparative Study of Artificial Bee Colony, Simulated Annealing, and Hill Climbing
Abstract
Glass identification is essential in forensic science and industrial uses. However, the effectiveness of classification algorithms heavily relies on selecting the correct hyperparameters and relevant features. This study investigates the efficacy of three optimization algorithms—Artificial Bee Colony (ABC), Simulated Annealing (SA), and Hill Climbing (HC)—for optimizing the k value, which is the neighbor value of the k-Nearest Neighbors (k-NN) classifier, the distance measure expressing the proximity between two samples, and the feature subset of the UCI Glass Identification dataset. By reframing the classification problem as a multi-dimensional optimization task, the algorithms are assessed based on accuracy, precision, recall, and F1 score. The results show that the population-based ABC algorithm, as a meta-heuristic approach, outperforms local search methods by avoiding local optima and effectively reducing the dimensionality of the data. RI (refractive index) and Mg (magnesium oxide content) emerge as particularly discriminative features. The study demonstrates that combining automatic parameter tuning with feature selection can substantially enhance the predictive performance of k-NN in complex classification tasks.
Keywords
Glass classification K-nearest neighbors Artificial bee colony Simulated annealing Hill climbing Optimization
Ethical Statement
Ethics committee approval was not required for this study because of there was no study on animals or humans.
Supporting Institution
This study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Project Number
Yok / Bulunmamaktadır
References
- Al-Betar, M. A., Hammouri, A. I., Awadallah, M. A., & Doush, I. A. (2021). Binary β-hill climbing optimizer with S-shape transfer function for feature selection. Journal of Ambient Intelligence and Humanized Computing, 12(7), 7637–7665. https://doi.org/10.1007/s12652-020-02484-z
- Ay, Ş., Ekinci, E., & Garip, Z. (2023). A comparative analysis of meta-heuristic optimization algorithms for feature selection on ML-based classification of heart-related diseases. The Journal of Supercomputing, 79(11), 11797–11826. https://doi.org/10.1007/s11227-023-05132-3
Simultaneous Feature Selection and Hyperparameter Tuning of K-Nearest Neighbors for Glass Classification: A Comparative Study of Artificial Bee Colony, Simulated Annealing, and Hill Climbing
Abstract
Glass identification is essential in forensic science and industrial uses. However, the effectiveness of classification algorithms heavily relies on selecting the correct hyperparameters and relevant features. This study investigates the efficacy of three optimization algorithms—Artificial Bee Colony (ABC), Simulated Annealing (SA), and Hill Climbing (HC)—for optimizing the k value, which is the neighbor value of the k-Nearest Neighbors (k-NN) classifier, the distance measure expressing the proximity between two samples, and the feature subset of the UCI Glass Identification dataset. By reframing the classification problem as a multi-dimensional optimization task, the algorithms are assessed based on accuracy, precision, recall, and F1 score. The results show that the population-based ABC algorithm, as a meta-heuristic approach, outperforms local search methods by avoiding local optima and effectively reducing the dimensionality of the data. RI (refractive index) and Mg (magnesium oxide content) emerge as particularly discriminative features. The study demonstrates that combining automatic parameter tuning with feature selection can substantially enhance the predictive performance of k-NN in complex classification tasks.
Keywords
Glass classification K-nearest neighbors Artificial bee colony Simulated annealing Hill climbing Optimization
Ethical Statement
Ethics committee approval was not required for this study because of there was no study on animals or humans.
Supporting Institution
This study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Project Number
Yok / Bulunmamaktadır
References
- Al-Betar, M. A., Hammouri, A. I., Awadallah, M. A., & Doush, I. A. (2021). Binary β-hill climbing optimizer with S-shape transfer function for feature selection. Journal of Ambient Intelligence and Humanized Computing, 12(7), 7637–7665. https://doi.org/10.1007/s12652-020-02484-z
- Ay, Ş., Ekinci, E., & Garip, Z. (2023). A comparative analysis of meta-heuristic optimization algorithms for feature selection on ML-based classification of heart-related diseases. The Journal of Supercomputing, 79(11), 11797–11826. https://doi.org/10.1007/s11227-023-05132-3
Details
| Primary Language | English |
|---|---|
| Subjects | Optimization Techniques in Mechanical Engineering |
| Journal Section | Research Article |
| Authors | |
| Project Number | Yok / Bulunmamaktadır |
| Submission Date | December 14, 2025 |
| Acceptance Date | January 14, 2026 |
| Publication Date | January 15, 2026 |
| Published in Issue | Year 2026 Volume: 9 Issue: 1 |