用流式数据和滑动窗口提升电竞胜率预测准确率，结果可解释。
Explainable e-sports win prediction through Machine Learning classification in streaming
- 通过滑动窗口动态捕捉游戏关键变化
- 准确率超90%，优于现有方法
- 适合需要透明决策的排名与推荐系统
电子竞技观众和玩家数量持续增长，配合优化的通信方案与云计算技术，推动了在线游戏产业的发展。尽管人工智能在电竞分析中常用于从数据中提取有意义模式并可视化以辅助决策，但现有专业胜率预测研究多聚焦于批处理分类，忽视了可视化与实时性。本文提出一种面向流式数据的可解释胜率预测分类方法，通过多个滑动窗口控制输入数据，反映游戏过程中的关键变化。实验结果显示，准确率超过90%，优于文献中已有方案。系统凭借可解释模块，可被用于排名与推荐系统，增强对预测结果的信任度。
原文摘要 · Abstract (English)
The increasing number of spectators and players in e-sports, along with the development of optimized communication solutions and cloud computing technology, has motivated the constant growth of the online game industry. Even though Artificial Intelligence-based solutions for e-sports analytics are traditionally defined as extracting meaningful patterns from related data and visualizing them to enhance decision-making, most of the effort in professional winning prediction has been focused on the classification aspect from a batch perspective, also leaving aside the visualization techniques. Consequently, this work contributes to an explainable win prediction classification solution in streaming in which input data is controlled over several sliding windows to reflect relevant game changes. Experimental results attained an accuracy higher than 90 %, surpassing the performance of competing solutions in the literature. Ultimately, our system can be leveraged by ranking and recommender systems for informed decision-making, thanks to the explainability module, which fosters trust in the outcome predictions.
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