| Title | A machine learning approach to predict the result of League of Legends |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Shen, Qiyuan |
| Conference Name | 2022 International Conference on Machine Learning and Knowledge Engineering (MLKE) |
| Keywords | Asia, composability, Games, Knowledge engineering, League of Legends, machine learning, machine learning algorithms, MOBA, Prediction algorithms, privacy, pubcrawl, resilience, Resiliency, Support vector machines, Voting Classifier |
| Abstract | Nowadays, the MOBA game is the game type with the most audiences and players around the world. Recently, the League of Legends has become an official sport as an e-sport among 37 events in the 2022 Asia Games held in Hangzhou. As the development in the e-sport, analytical skills are also involved in this field. The topic of this research is to use the machine learning approach to analyze the data of the League of Legends and make a prediction about the result of the game. In this research, the method of machine learning is applied to the dataset which records the first 10 minutes in diamond-ranked games. Several popular machine learning (AdaBoost, GradientBoost, RandomForest, ExtraTree, SVM, Naive Bayes, KNN, LogisticRegression, and DecisionTree) are applied to test the performance by cross-validation. Then several algorithms that outperform others are selected to make a voting classifier to predict the game result. The accuracy of the voting classifier is 72.68%. |
| DOI | 10.1109/MLKE55170.2022.00013 |
| Citation Key | shen_machine_2022 |
A machine learning approach to predict the result of League of Legends