使用一类SVM计算异常分数以进行异常检测 [英] Calculating anomaly score for Anomaly detection using One-Class SVM

问题描述

我对使用One-Class SVM计算异常检测的异常分数有疑问.我的问题是:如何使用Decision_function(X)来计算它，就像我在隔离林中计算异常得分的方法一样? 非常感谢

I have a question about Calculating anomaly score for Anomaly detection using One-Class SVM. My question is that: How can I calculate it using decision_function(X), just the way I calculate anomaly score in Isolation forest? Thanks a lot,

推荐答案

在隔离森林中，异常分数是衡量从特定路径中选择出一个特定观测值所需的平均路径长度与所需的平均路径长度之间的偏差的量度.挑出正常"观察结果

In Isolation Forests, anomaly score is a measure of the deviation of average length of the path required to single out a particular observation from the average length of path required to single out a "normal" observation

这里的平均值是使用的所有不同树的平均值.由于SVM不是整体方法-这种异常评分的概念并不直接适用.

The average here is taken over all the different trees that are used. Since SVM is not an ensemble method - this notion of anomaly score does not directly apply.

一种方法，而且我不知道这在统计学上/科学上听起来如何，测量异常得分是基于预测变量的子集建立多个SVM分类器.然后，您可以将特定点被分类为异常值的次数的百分比用作异常分数的代理.

One way, and I don't know how statistically/scientifically sound this is, of measuring an anomaly score is to build multiple SVM classifiers based on a subset of predictors. You could then use the percentage of times a particular point is classified as an outlier as a proxy for an anomaly score.

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