论文标题
由信心校准的MOBA游戏获胜者预测器
A Confidence-Calibrated MOBA Game Winner Predictor
论文作者
论文摘要
在本文中，我们提出了一种置信度校准方法，以预测英雄联盟著名的多人在线战场（MOBA）游戏的获胜者。在MOBA游戏中，数据集可能包含大量取决于输入的噪声；并非所有这样的噪音都是可以观察到的。因此，希望尝试进行置信度的预测。不幸的是，大多数现有的置信度校准方法与图像和文档分类任务有关，而对不确定性的考虑并不重要。在本文中，我们提出了一种新颖的校准方法，该方法考虑了数据不确定性。与传统的ECE值为1.11％的常规温度缩放方法相比，所提出的方法实现了出色的预期校准误差（ECE）（ECE）（0.57％），主要是由于数据不确定性考虑。
In this paper, we propose a confidence-calibration method for predicting the winner of a famous multiplayer online battle arena (MOBA) game, League of Legends. In MOBA games, the dataset may contain a large amount of input-dependent noise; not all of such noise is observable. Hence, it is desirable to attempt a confidence-calibrated prediction. Unfortunately, most existing confidence calibration methods are pertaining to image and document classification tasks where consideration on uncertainty is not crucial. In this paper, we propose a novel calibration method that takes data uncertainty into consideration. The proposed method achieves an outstanding expected calibration error (ECE) (0.57%) mainly owing to data uncertainty consideration, compared to a conventional temperature scaling method of which ECE value is 1.11%.
