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    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/123520


    题名: 以機器學習預測電子競技玩家排名—以絕地求生為例
    其它题名: Esports Player Placement Prediction Using Machine Learning—— a Case Study of PlayerUnknown’s BattleGround
    作者: 周清江;劉蘊鋒
    关键词: 機器學習;lightGBM;絕地求生;坎德爾排名相關係數;斯皮爾曼排名相關係數;歸一化折損累計增益;Machine learning;lightGBM;PUBG;Kendall rank correlation coefficient;Spearman rank correlation coefficient;Normalized discounted cumulative gain
    日期: 2022-08-27
    上传时间: 2023-04-28 18:32:23 (UTC+8)
    摘要: 電子競技近年來極受歡迎,其比賽排名預測是很重要的新興研究主題。“絕地求生"是近年來一款十分流行的大型團隊合作射擊遊戲。本研究的主旨是基於 PUBG 比賽表現
    特徵與排位分數特徵建立預測模型並使用其他工具分析資料集中不同種類的特徵對模型的影響。我們使用 lightGBM 回歸模型預測玩家排名時非常準確,預測出現排名反轉
    現象也保持在很低的程度。在評價模型預測結果時,我們選擇使用坎德爾排名相關係數作為模型預測評價指標。我們分析了三個遊戲模式中不同特徵對模型的影響的大小,並評比不同特徵第一名的預測結果。結果表明增加排位分數特徵、重要表現特徵和他們的群組特徵都可以提高模型的預測能力。
    E-sports has become very popular in recent years, and the prediction of match rankings is an important emerging research topic. "Player Unknown Battle Ground" (PUBG) is one of the most popular multiplayer battle royale games in recent years. The main objective of this study is to build predictive models based on PUBG match performance features and ranking score features. We also investigate group characteristics of important features to analyze their effects in the prediction. We use the lightGBM regression model for the prediction, since lightGBM has performed well in other studies. Our model has an excellent result in predicting player rankings. Additionally, cases of rank reversals in our results are rare. We use the Kendall rank correlation coefficient、Spearman rank correlation coefficient and Normalized Discounted Cumulative Gain as the model prediction evaluation metric. We analyze the influence of different features in the three game modes: solo、duo and quad, and evaluated the prediction results of the winner of each game. The results show that adding ranking score features, important features and their group characteristics can improve the prediction performance of the model.
    显示于类别:[資訊管理學系暨研究所] 會議論文

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