异常检测算法的相关内容

问答 2021-12-13 来自：开发者社区

一、背景与挑战在传统的监控、智能运维领域，用户想去监控某一个业务指标往往采用环比、同比的阈值配置方式。这在实践中面临不少问题：阈值应该配成多少，不同的业务甚至不同的时间段都需要不同的阈值业务总是出现周期性的趋势，规则配置极易产生误报特殊的业务场景例如活动引起的冲高回落、大促的冲击都会引起大量误报对大量维度的业务，比如上百/上千个不同类型的错误码，大量接口调用量等指标该如何分别配置？复杂的业务衍生....

文章 2019-03-12 来自：开发者社区

[机器学习][异常检测算法]

0.引言异常检测应用在工业检测、账户行为监测等领域。问题特点：样本比例高度不均衡，异常点总是极少数的；异常样本子集一般不具备共性的特征，异常的方式各不相同，难以作为一个类别分类高维数据中，并非所有的样本都会用到，需要特征选择；由于样本高度不平衡，因此测试的指标往往用F1；异常检测问题，往往更多使用无监督的算法建模，再结合标定的验证集用于切阈值。异常检测的两类常用方法：基...

文章 2017-08-01 来自：开发者社区

机器学习-异常检测算法（二）：Local Outlier Factor

Local Outlier Factor（LOF）是基于密度的经典算法（Breuning et.al. 2000）, 文章发表于 SIGMOD 2000, 到目前已经有 3000+ 的引用。在 LOF 之前的异常检测算法大多是基于统计方法的，或者是借用了一些聚类算法用于异常点的识别（比如，DBSCAN，OPTICS）。但是，基于统计的异常检测算法通常需要假设数据服从特定的概率分布，这个假设往.....

文章 2017-07-12 来自：开发者社区

机器学习-异常检测算法（一）：Isolation Forest

"An outlier is an observation which deviates so much from other observations as to arouse suspicions that it was generated by a different mechanism." — D. M. Hawkins, ***Identification of Outli...

文章 2017-04-14 来自：开发者社区

局部异常因子与KL散度异常检测算法简述

Local Outlier Factor Given local outlier factors, we can detect the outliers that are always away from most of the samples. In order to outline the algorithm, some concepts must go first: Reachabili.....

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