Birant Kokten Ulas, Birant Derya
Department of Computer Engineering, Dokuz Eylul University, Izmir 35390, Turkey.
Entropy (Basel). 2022 Dec 23;25(1):28. doi: 10.3390/e25010028.
The aim of this study is to develop a new approach to be able to correctly predict the outcome of electronic sports (eSports) matches using machine learning methods. Previous research has emphasized player-centric prediction and has used standard (single-instance) classification techniques. However, a team-centric classification is required since team cooperation is essential in completing game missions and achieving final success. To bridge this gap, in this study, we propose a new approach, called (MOMIL). It is the first study that applies the multi-instance learning technique to make win predictions in eSports. The proposed approach jointly considers the objectives of the players in a team to capture relationships between players during the classification. In this study, was used as a measure to determine the impurity (uncertainty) of the training dataset when building decision trees for classification. The experiments that were carried out on a publicly available eSports dataset show that the proposed multi-objective multi-instance classification approach outperforms the standard classification approach in terms of accuracy. Unlike the previous studies, we built the models on season-based data. Our approach is up to 95% accurate for win prediction in eSports. Our method achieved higher performance than the state-of-the-art methods tested on the same dataset.
本研究的目的是开发一种新方法，以便能够使用机器学习方法正确预测电子竞技比赛的结果。先前的研究强调以玩家为中心的预测，并使用标准（单实例）分类技术。然而，由于团队合作对于完成游戏任务和取得最终成功至关重要，因此需要以团队为中心的分类。为了弥补这一差距，在本研究中，我们提出了一种名为（MOMIL）的新方法。这是第一项应用多实例学习技术在电子竞技中进行胜负预测的研究。所提出的方法在分类过程中联合考虑团队中玩家的目标，以捕捉玩家之间的关系。在本研究中，当构建用于分类的决策树时，使用 作为衡量训练数据集杂质（不确定性）的指标。在一个公开可用的电子竞技数据集上进行的实验表明，所提出的多目标多实例分类方法在准确性方面优于标准分类方法。与先前的研究不同，我们基于赛季数据构建模型。我们的方法在电子竞技胜负预测方面的准确率高达95%。我们的方法在同一数据集上测试时比现有最先进的方法表现更优。
