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A Confidence-Calibrated MOBA Game Winner Predictor

Title
A Confidence-Calibrated MOBA Game Winner Predictor
Author
정기석
Keywords
Esports; MOBA game; League of Legends; Winning Probability; Confidence-Calibration
Issue Date
2020-10
Publisher
IEEE
Citation
2020 IEEE Conference on Games (CoG), page. 622-625
Abstract
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%.
URI
https://ieeexplore.ieee.org/document/9231878?arnumber=9231878&SID=EBSCO:edseeehttps://repository.hanyang.ac.kr/handle/20.500.11754/171270
ISBN
978-1-7281-4533-4
ISSN
2325-4270
DOI
10.1109/CoG47356.2020.9231878
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > ELECTRONIC ENGINEERING(융합전자공학부) > Articles
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