我们对概念漂移了解的一两件事 - - 一项关于在不断变化的环境中监测的调查. 部分A:检测概念的漂移
Fabian Hinder1, Valerie Vaquet1, Barbara Hammer1
1Faculty of Technology, Bielefeld University, Bielefeld, North Rhine-Westphalia, Germany.
Frontiers in artificial intelligence
|July 4, 2024
概括
在无监督数据流中检测概念漂移对于工业监测和异常检测至关重要. 本综述为分析工程中的漂移检测策略提供了分类和标准化实验.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 工程 工程师 工程师 工程师
背景情况:
- 概念漂移或随着时间的推移而发生的数据变化会影响工业流程,并可能导致安全关键的故障.
- 检测和分析概念漂移对于可靠的系统运行至关重要.
- 现有的文献主要涵盖监督学习,忽略了无监督数据流.
研究的目的:
- 提供关于无监督数据流中的概念漂移检测的综合文献综述.
- 通过专注于无监督环境来弥补现有调查的差距.
- 为评估无监督漂移检测方法提供分类学,精确的定义和标准化实验.
主要方法:
- 在无监督数据流中探测概念漂移的文献综述.
- 为现有的无监督漂移检测工作制定分类法.
- 使用参数人工数据集进行方法比较的标准化实验的设计.
主要成果:
- 一个关于无监督概念漂移检测技术的全面概述.
- 一个结构化的分类系统,对现有方法进行分类.
- 标准化实验结果,促进系统分析和检测策略的比较.
结论:
- 无监督概念漂移检测对于监控和异常检测等工程任务非常重要.
- 该调查为对不同检测方案的系统分析提供了基础.
- 在现实工程场景中应用无监督漂移检测的指导方针可以从该研究中得出.
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