机器学习方法的比较,使用快速马射线中子激活数据对放射性元素进行分类
Jino Mathew1, Rohit Kshirsagar2, Dzariff Z Abidin3
1Faculty of Engineering, Environment and Computing, Coventry University, Priory Street, Coventry, CV1 5FB, UK. jino.mathew@coventry.ac.uk.
Scientific reports
|June 19, 2023
概括
机器学习算法通过中子捕获提示 - 马激活分析 (PGAA) 提高了放射性材料的检测. 在分类放射性元素方面,AdaBoost表现出卓越的性能,减少了核安全应用中的假阳性.
科学领域:
- 核安全是核安全.
- 分析化学是一种分析化学.
- 机器学习 机器学习
背景情况:
- 非法放射性材料的检测对于核安全至关重要.
- 中子捕获提示性玛激活分析 (PGAA) 检测到各种放射性材料.
- 目前的PGAA方法面临着长时间检测和高假阳性率的挑战.
研究的目的:
- 开发和评估机器学习算法,用于从PGAA光谱中分类放射性元素.
- 比较不同算法的有效性,特别是对不平衡的数据集.
- 确定PGAA光谱数据分析的最佳分类器.
主要方法:
- 六个机器学习算法被开发用于分类.
- 使用精度,回忆,F1分数,特异性,混矩阵,ROC-AUC和几何平均分数 (GMS) 等指标来评估性能.
- 分析侧重于适用于不平衡数据集的算法.
主要成果:
- 基于树的算法 (决策树,随机森林,AdaBoost) 超过了支持矢量机和K-最近邻居.
- 在少数群体中,AdaBoost显示了最高的召回率和最小的虚假负值.
- 该研究确定AdaBoost是PGAA光谱数据的首选分类器.
结论:
- 机器学习,特别是AdaBoost,使用PGAA数据显著提高了放射性元素的分类.
- 这种方法解决了传统PGAA方法的局限性,提高了核安全的准确性和效率.
- 在处理不平衡的数据集方面,AdaBoost的性能使其成为现实世界核取证应用的理想选择.
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