数据分布和引导设置对使用隔离森林在过程质量控制中的异常检测的影响
1School of Industrial and Management Engineering, Korea University, Seoul 02841, Republic of Korea.
Entropy (Basel, Switzerland)
|July 29, 2025
概括
这项研究表明,隔离森林 (iForest) 在统计过程控制中的异常检测方面表现出色,优于传统方法,特别是非正常数据. 引导重抽样提供了轻微的收益,但iForest在微妙的流程转移中扎.
科学领域:
- 统计过程控制 统计过程控制
- 机器学习 机器学习
- 数据挖掘 数据挖掘
背景情况:
- 隔离森林 (iForest) 是一个流行的异常检测算法.
- 它在非正常数据分布和引导重新采样方面的性能尚未得到充分理解.
- 统计过程控制 (SPC) 从强大的异常检测方法中受益.
研究的目的:
- 评估数据分布对iForest异常检测性能的影响.
- 评估引导重抽样对iForest的影响.
- 在SPC中将iForest与Hotelling的T2控制图进行比较.
主要方法:
- 一个模拟研究跨越18个场景与日志正常,马和t分布.
- 测试了各种各样的平均转移级别和引导配置.
- 性能指标包括准确性,精度,回忆,F1分数和平均运行长度 (ARL) 1.
主要成果:
- iForest的表现明显优于Hotelling的T2,特别是在非高斯数据和中小转移方面.
- 引导重新抽样在分类指标上取得了微不足道的改进.
- iForest对微妙的过程变化 (例如,1σ平均转移) 的敏感性降低.
结论:
- iForest是SPC中异常检测的强大工具,特别是对于非正常数据.
- 引导重抽样提供有限的好处.
- 提高iForest对微小工艺变化的灵敏度是未来研究的一个关键领域.
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