Outcome prediction of DOTA2 based on Naïve Bayes classifier

Although DOTA2 is a popular game around the world, no clear algorithm or software are designed to forecast the winning probability by analyzing the lineups. However, the author finds that Naive Bayes classifier, one of the most common classification algorithm, can analyze the lineups and predict the...

Full description

Saved in:
Bibliographic Details
Published in2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS) pp. 591 - 593
Main Authors Kaixiang Wang, Wenqian Shang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2017
Subjects
Algorithm design and analysis
Classification Algorithm
Classification algorithms
DOTA2
Games
Machine learning algorithms
Naive Bayes
Probability
Sun
Training
Online AccessGet full text

Cover

More Information
Summary:Although DOTA2 is a popular game around the world, no clear algorithm or software are designed to forecast the winning probability by analyzing the lineups. However, the author finds that Naive Bayes classifier, one of the most common classification algorithm, can analyze the lineups and predict the outcome according to the lineups and gives an improved Naive Bayes classifier. Using the DOTA2 data set published in the UCI Machine Learning Repository, we test Naive Bayes classifier's prediction of respective winning probability of both sides in the game. The results show that Naive Bayes classifier is a practical tool to analyze the lineups and predict the outcome based on players' choices.
DOI:10.1109/ICIS.2017.7960061