Citation

Al-Andoli, Mohammed Nasser and Ahmed Alomari, Mohammad and Alwayle, Ibrahim M and Hasan Al-kumaim, Nabil and Alsayaydeh, Jamil Abedalrahim Jamil and Nuraini Che Ku Mohd, Che Ku (2025) A Hybrid Parallel Ensemble Learning Framework with Fuzzy ARTMAP to Improve Outcome Prediction in Professional Esports. Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, 50 (3). ISSN 1064-1246

Text A Hybrid Parallel Ensemble Learning Framework.pdf - Published Version Restricted to Repository staff only Download (204kB)

Abstract

Esports is a rapidly growing industry with a massive viewership, generating a wealth of data that can be leveraged to improve win prediction. Machine learning approaches have been employed for game outcome prediction. However, they have typically focused on a single classifier/predictor, which exhibits effectiveness and scalability issues due to intricate architectures and a lack of parallel processing. This paper proposes a new hybrid parallel ensemble learning framework combined with the Fuzzy ARTMAP (FAM) model to enhance win prediction in professional esports. The proposed framework leverages parallel computing and combines diverse deep neural network (DNN) and machine learning (ML) models as base learners, with FAM serving as the meta-learner to effectively integrate and train the base learners outputs. Empirical studies using a c demonstrate the superior efficacy and efficiency of this approach compared to conventional methods. The results show that HELP-GOP is effective and efficient, achieving a high-performance level with 98.49% accuracy and a speedup of up to 5.2× with parallel processing. Additionally, the results demonstrate that hybrid parallel ensemble learning represents a significant advancement in win prediction for professional esports, showcasing more accurate and sophisticated predictive analytics in the esports field.

Item Type:	Article
Uncontrolled Keywords:	Deep neural network, ensemble learning, esports, fuzzy ARTMAP, machine learning, parallel computing
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Nor Afiqah Mohd Adnan
Date Deposited:	02 Dec 2025 04:45
Last Modified:	05 May 2026 04:09
URII:	http://shdl.mmu.edu.my/id/eprint/14931

Downloads

Edit (login required)