基于人工智能的投注异常检测系统,以确保体育中的公平,防止非法博
Changgyun Kim1, Jae-Hyeon Park2, Ji-Yong Lee3
1Department of Artificial Intelligence & Software, Kangwon National University, Samcheok, 25913, Republic of Korea.
Scientific reports
|March 19, 2024
概括
这项研究引入了机器学习模型,用于检测使用投注赔率的体育比赛调节. 集成模型,随机森林模型和KNN模型的准确度超过92%,超过了以前的方法.
科学领域:
- 运动分析 运动分析
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 体育比赛的操纵对体育的完整性构成重大威胁.
- 检测体育博彩中的异常对于保持公平竞争至关重要.
- 现有的检测比赛调节的方法往往缺乏足够的准确性.
研究的目的:
- 开发和评估机器学习模型,以实时检测体育比赛的操纵.
- 为了比较后勤回归,随机森林,支向量机,k-最近邻居和集合模型在识别匹配纠正异常方面的性能.
- 为了利用体育博彩赔率数据在足球比赛中检测异常.
主要方法:
- 利用了来自12家公司的世界足球联赛比赛投注数据的综合数据库.
- 实现了五种不同的机器学习模型:逻辑回归 (LR),随机森林 (RF),支持矢量机 (SVM),k-最近邻居 (KNN) 和优化集群模型.
- 训练有素的模型对历史的投注赔率和比赛结果数据进行训练,以确定正常和异常比赛的模式.
- 使用训练过的模型开发了一个实时检测系统.
主要成果:
- 随机森林 (RF),K-最近邻居 (KNN) 和整体模型表现出高精度,超过92%.
- 后勤回归 (LR) 和支持向量机 (SVM) 模型实现了大约80%的准确性.
- 开发的模型显著超过了以前的研究,这些研究报告了使用单个模型的 70-80% 范围内的准确性.
结论:
- 机器学习模型,特别是集体,射频和KNN模型,在检测基于投注赔率的体育比赛安排异常方面非常有效.
- 开发的实时检测系统在保护体育诚信的现有方法上提供了显著的改进.
- 该研究强调了利用广泛的投注数据和先进的机器学习技术在体育运动中强有力的异常检测的潜力.
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