Predicting Bid Verification in Spectrum Auctions: A Data-Driven Approach

Year 2025, Volume: 14 Issue: 3, 1420 - 1439, 30.09.2025

Ceren Nisa Avcu , Ali Değirmenci , Ömer Karal

https://doi.org/10.17798/bitlisfen.1650456

Abstract

Spectrum auctions are very important for the strategic allocation of frequency bands in the telecommunications industry, ensuring efficient and fair access to this valuable resource. However, the complexity of auction environments—characterized by vast state spaces and multidimensional bid attributes—renders manual bid verification infeasible. This study introduces an innovative, data-driven approach by utilizing machine learning models, including k-nearest neighbors, support vector machines, decision trees, and stochastic gradient descent classifiers, to automate the verification process. Through hyperparameter tuning and rigorous k-fold cross-validation, the decision tree model emerged as the most effective, achieving an F1-score of 96% and a G-Mean of 97%. These results demonstrate the practical viability of AI-enhanced verification systems in spectrum auctions and suggest broader applicability across various high-stakes auction platforms where real-time, reliable validation is essential.

Keywords

Classification , Machine Learning , Spectrum Auctions

References

• J. Bailey, “Can machine learning predict the price of art at auction?,” Harvard Data Science Review, vol. 2, no. 2, pp. 1-8,2020.
• M. J. G. Rodríguez, V. Rodríguez-Montequín, P. Ballesteros-Pérez, P. E., Love, and R. Signor, “Collusion detection in public procurement auctions with machine learning algorithms,” Automation in Construction, vol. 133, p. 104047, 2022. doi:10.1016/j.autcon.2021.104047
• D. Imhof and H. Wallimann, “Detecting bid-rigging coalitions in different countries and auction formats,” International Review of Law and Economics, vol. 68, p. 106016, 2021. doi: 10.1016/j.irle.2021.106016
• W. U. H. Abidi, M. S. Daoud, B. Ihnaini, M. A. Khan, T. Alyas, A. Fatima, and M. Ahmad, “Real-time shill bidding fraud detection empowered with fussed machine learning,” IEEE Access, vol. 9, pp. 113612-113621, 2021. doi: 10.1109/ACCESS.2021.3098628.
• R. Zhang, C. Jiang, J. Zhang, J. Fan, J. Ren, and H. Xia, “Reinvigorating sustainability in Internet of Things marketing: Framework for multi-round real-time bidding with game machine learning,” Internet of Things, vol. 24, p. 100921, 2023. doi: 10.1016/j.iot.2023.100921
• J. Rani P., A. Kulkarni, A. V. Kamath, A. Menon, P. Dhatwalia and D. Rishabh, “Prediction of Player Price in IPL Auction Using Machine Learning Regression Algorithms,” 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Bangalore, India, pp. 1-6, 2020. doi: 10.1109/CONECCT50063.2020.9198668
• W. Kusonkhum, K. Srinavin, and T. Chaitongrat, “The adoption of a big data approach using machine learning to predict bidding behavior in procurement management for a construction project,” Sustainability, vol. 15, no. 17, p. 12836, 2023. doi: 10.3390/su151712836
• P. V. d. C. Souza and M. Dragoni, “Knowledge extraction in auction verification employing techniques from machine learning and fuzzy neural networks.,” 2024 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS), Madrid, Spain, pp. 1-8, 2024. doi: 10.1109/EAIS58494.2024.10569109.
• M. Tajabadi and D. Heider, “Fair swarm learning: Improving incentives for collaboration by a fair reward mechanism,” Knowledge-Based Systems, vol. 304, p. 112451, 2024. doi: 10.1016/j.knosys.2024.112451
• E. Ordoni, J. Bach and A. -K. Fleck, “Analyzing and Predicting Verification of Data-Aware Process Models–A Case Study With Spectrum Auctions,” IEEE Access, vol. 10, pp. 31699-31713, 2022. doi: 10.1109/ACCESS.2022.3154445
• E. Ordoni, J. Mülle, K. Yang, and K. Böhm, “Efficient Verification of Process Models Supporting Modifications of Data Values,” 2022 IEEE 24th Conference on Business Informatics (CBI), Amsterdam, Netherlands, pp. 21-30, 2022. doi: 10.1109/CBI54897.2022.00010.
• S. F. Fischer, M. Feurer, and B. Bischl, “OpenML-CTR23–a curated tabular regression benchmarking suite,” Proc. AutoML Conf. 2023 (Workshop), 2023.
• S. C. Tan, “Enhancing regression tree predictions with terminal-node anomaly detection,” 2023 6th Artificial Intelligence and Cloud Computing Conf., pp. 21-26, 2023. doi: 10.1145/3639592.3639596
• L. Bottou, F. E. Curtis, and J. Nocedal, “Optimization methods for large-scale machine learning,” SIAM Review, vol. 60, no. 2, pp. 223-311, 2018. doi:10.1137/16M1080173
• A. Ozbay, A. Degirmenci, and O. Karal, “A Comparative Analysis of Machine Learning Algorithms for Accurate Step Detection in Wrist Worn Devices,” 2023 Innovations in Intelligent Systems and Applications Conference (ASYU), Sivas, Turkiye, pp. 1-5, 2023. doi: 10.1109/ASYU58738.2023.10296741.
• A. Degirmenci and O. Karal, “iMCOD: Incremental multi-class outlier detection model in data streams,” Knowledge-Based Systems, vol. 258, p. 109950, 2022. doi: 10.1016/j.knosys.2022.109950
• T. Hastie, R. Tibshirani, J. H. Friedman, and J. H. Friedman, The elements of statistical learning: data mining, inference, and prediction, vol. 2, New York: Springer, 2009, pp. 1-758.
• M. Çakır, A. Degirmenci, and O. Karal, “Exploring the behavioural factors of cervical cancer using ANOVA and machine learning techniques,” Int. Conf. Science, Engineering Management and Information Technology, Feb. 2022, pp. 249-260, Cham: Springer Nature Switzerland.
• A. N. Karaoglu, H. Caglar, A. Degirmenci, and O. Karal, “Performance Improvement with Decision Tree in Predicting Heart Failure,” 2021 6th International Conference on Computer Science and Engineering (UBMK), Ankara, Turkey, pp. 781-784, 2021. doi: 10.1109/UBMK52708.2021.9558939.
• M. Muttaqi, A. Degirmenci, and O. Karal, “US Accent Recognition Using Machine Learning Methods,” 2022 Innovations in Intelligent Systems and Applications Conference (ASYU), Antalya, Turkey, pp. 1-6, 2022. doi: 10.1109/ASYU56188.2022.9925265.
• W. S. Noble, “What is a support vector machine?,” Nature Biotechnology, vol. 24, no. 12, pp. 1565-1567, 2006. doi: 10.1038/nbt1206-1565
• Ö. Karal, “Performance comparison of different kernel functions in SVM for different k value in k-fold cross-validation,” 2020 Innovations in Intelligent Systems and Applications Conference (ASYU), Istanbul, Turkey, pp. 1-5, 2020. doi: 10.1109/ASYU50717.2020.9259880
• Değirmenci, A. “Machine Learning Models for Accurate Prediction of Obesity: A Data-Driven Approach,” Turkish Journal of Science and Technology, vol. 20, no. 1, pp. 77-90, 2025. doi: 10.55525/tjst.1572382
• S. García and F. Herrera, “Evolutionary undersampling for classification with imbalanced datasets: Proposals and taxonomy,” Evolutionary Computation, vol. 17, no. 3, pp. 275-306, 2009. doi: 10.1162/evco.2009.17.3.275
• E. Ordoni, J. Bach, A.-K. Fleck, and J. Bach, “Auction verification,” UCI Machine Learning Repository, 2022. [Online]. Available: https://doi.org/10.24432/C52K6N.Ge