Game Classification and Analysis Based on Machine Learning-Based Methods

Zhen Li, Dingzhuoya Wang, Yanzhao Zou

2024

Abstract

It is clear that we should pay high attention to video games on a variety of degrees. This includes the rating of them (Game rating). The efficiency for labor to do this work is highly limited and the reliability is unstable, so try to let Artificial Intelligence (AI) do this work or assist related personnel. The goal is to construct an AI that can help evaluate the ratings of each video game with basic details of the contents that the game contains. We try different AI models and use datasets on Kaggle for AI training. We also take multiple indicators such as accuracy to compare the performance of those models. This study is conducted on those datasets on Kaggle, the result shows that Extreme gradient boosting (XGboost) has advantages over others to some degree. XGboost improves data fitting and inference due to its powerful representation ability. The proposed plan can help staff improve labor efficiency and reliability in-game rating.

Paper Citation

in Harvard Style

Li Z., Wang D. and Zou Y. (2024). Game Classification and Analysis Based on Machine Learning-Based Methods. In Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-690-3, SciTePress, pages 382-386. DOI: 10.5220/0012866800004547

in Bibtex Style

@conference{icdse24,
author={Zhen Li and Dingzhuoya Wang and Yanzhao Zou},
title={Game Classification and Analysis Based on Machine Learning-Based Methods},
booktitle={Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2024},
pages={382-386},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012866800004547},
isbn={978-989-758-690-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - Game Classification and Analysis Based on Machine Learning-Based Methods
SN - 978-989-758-690-3
AU - Li Z.
AU - Wang D.
AU - Zou Y.
PY - 2024
SP - 382
EP - 386
DO - 10.5220/0012866800004547
PB - SciTePress