## A Classification Framework for Anomaly Detection

** Ingo Steinwart, Don Hush, Clint Scovel**; 6(8):211−232, 2005.

### Abstract

One way to describe anomalies is by saying that anomalies
are not concentrated. This leads to the problem of finding
level sets for the data generating density. We interpret this
learning problem as a binary classification problem and compare
the corresponding classification risk with the standard
performance measure for the density level problem. In particular
it turns out that the empirical classification risk can serve as
an empirical performance measure for the anomaly detection problem.
This allows us to compare different anomaly detection algorithms
*empirically*, i.e. with the help of a test set. Furthermore,
by the above interpretation we can give a strong justification for
the well-known heuristic of artificially sampling "labeled" samples,
provided that the sampling plan is well chosen. In particular this
enables us to propose a support vector machine (SVM) for anomaly
detection for which we can easily establish universal consistency.
Finally, we report some experiments which compare our SVM to other
commonly used methods including the standard one-class SVM.

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