SDE for Olympic selection Based on Dynamic Bayesian Network

Si Chen, Xiang Peng, Shixuan Xu
Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing, PMLR 278:476-482, 2025.

Abstract

This paper concentrates on the evaluation of Sports, Disciplines, or Events (SDEs) for Olympic selection. It presents a comprehensive approach that integrates multiple methods. The Dynamic Bayesian Network (DBN) is at the core, supplemented by data collection, normalization, and the TOPSIS method. This approach allows for a systematic assessment of SDEs, taking into account various criteria such as popularity, gender equity, and sustainability. The model’s outcomes provide valuable predictions for future Olympic SDE selection, and sensitivity analyses confirm its stability. The research proposes a data-centric approach for the International Olympic Committee (IOC) to refine and enhance the Olympic sports program, leveraging insights from AI and analytics.

Cite this Paper

BibTeX
@InProceedings{pmlr-v278-chen25c, title = {SDE for Olympic selection Based on Dynamic Bayesian Network}, author = {Chen, Si and Peng, Xiang and Xu, Shixuan}, booktitle = {Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing}, pages = {476--482}, year = {2025}, editor = {Zeng, Nianyin and Pachori, Ram Bilas and Wang, Dongshu}, volume = {278}, series = {Proceedings of Machine Learning Research}, month = {25--27 Apr}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v278/main/assets/chen25c/chen25c.pdf}, url = {https://proceedings.mlr.press/v278/chen25c.html}, abstract = {This paper concentrates on the evaluation of Sports, Disciplines, or Events (SDEs) for Olympic selection. It presents a comprehensive approach that integrates multiple methods. The Dynamic Bayesian Network (DBN) is at the core, supplemented by data collection, normalization, and the TOPSIS method. This approach allows for a systematic assessment of SDEs, taking into account various criteria such as popularity, gender equity, and sustainability. The model’s outcomes provide valuable predictions for future Olympic SDE selection, and sensitivity analyses confirm its stability. The research proposes a data-centric approach for the International Olympic Committee (IOC) to refine and enhance the Olympic sports program, leveraging insights from AI and analytics.} }
Endnote
%0 Conference Paper %T SDE for Olympic selection Based on Dynamic Bayesian Network %A Si Chen %A Xiang Peng %A Shixuan Xu %B Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing %C Proceedings of Machine Learning Research %D 2025 %E Nianyin Zeng %E Ram Bilas Pachori %E Dongshu Wang %F pmlr-v278-chen25c %I PMLR %P 476--482 %U https://proceedings.mlr.press/v278/chen25c.html %V 278 %X This paper concentrates on the evaluation of Sports, Disciplines, or Events (SDEs) for Olympic selection. It presents a comprehensive approach that integrates multiple methods. The Dynamic Bayesian Network (DBN) is at the core, supplemented by data collection, normalization, and the TOPSIS method. This approach allows for a systematic assessment of SDEs, taking into account various criteria such as popularity, gender equity, and sustainability. The model’s outcomes provide valuable predictions for future Olympic SDE selection, and sensitivity analyses confirm its stability. The research proposes a data-centric approach for the International Olympic Committee (IOC) to refine and enhance the Olympic sports program, leveraging insights from AI and analytics.
APA
Chen, S., Peng, X. & Xu, S.. (2025). SDE for Olympic selection Based on Dynamic Bayesian Network. Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing, in Proceedings of Machine Learning Research 278:476-482 Available from https://proceedings.mlr.press/v278/chen25c.html.

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