2020/06/28 by Donghee Kim, Kim, Dong-Hee, Chang-Woo Lee +3 · 1 citation
Computer Science · Social Sciences · #Artificial Intelligence in Games #Digital Games and Media #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Video Analysis and Summarization
paper · pdf · doi:10.48550/arxiv.2006.15521
openalex publication_date 2020/06/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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%.