@inproceedings{294aa3d1e2ce45158c2a4032eab1586c,
title = "A combination of general and specific models to predict victories in video games",
abstract = "At anytime in a tactics game, given the current status of the game players, it's always interesting to predict who is going to win the game in the end. This is the challenge defined in the IEEE BigData Cup 2021, where the objective is to predict the likelihood of winning the game, Tactical Troops: Anthracite Shift, given information of the players as well as logged data of the game at the time the prediction needs to be made. To address this challenge, we have developed a hybrid model that combines a general model trained on all the data and two specific models trained on particular data of two distinct game modes. Our implementation was realized by LightGBM, one of the most popular tree-based libraries implementing the state-of-the-art Extreme Gradient Boosting algorithm. In addition to combined models, a thoroughly feature engineering and feature selection together with a special handling of missing values and categorical features, a careful hyper-parameter tuning, and a robust cross-validation strategy to avoid overfitting issues helped us win the 2nd place in the competition.",
keywords = "Cross Validation, Feature Engineering, Feature Selection, Game Prediction, Gradient Boosting Trees, Hyperparameter Tuning, LightGBM",
author = "Vu, \{Quang Hieu\} and Dymitr Ruta and Ling Cen and Ming Liu",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Big Data, Big Data 2021 ; Conference date: 15-12-2021 Through 18-12-2021",
year = "2021",
doi = "10.1109/BigData52589.2021.9671285",
language = "British English",
series = "Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5683--5690",
editor = "Yixin Chen and Heiko Ludwig and Yicheng Tu and Usama Fayyad and Xingquan Zhu and Hu, \{Xiaohua Tony\} and Suren Byna and Xiong Liu and Jianping Zhang and Shirui Pan and Vagelis Papalexakis and Jianwu Wang and Alfredo Cuzzocrea and Carlos Ordonez",
booktitle = "Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021",
address = "United States",
}