A Hybrid Parallel Ensemble Learning Framework with Fuzzy ARTMAP to Improve Outcome Prediction in Professional Esports

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.

Keywords

esports ensemble learning machine learning fuzzy ARTMAP deep neural network parallel computing

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