Anomaly Detection

Anomaly detection is a kind of classification problem. Standard classification algorithms require the presence of both positive and negative examples (counterexamples) for a target class. One-Class Support Vector Machine (SVM) classification requires only the presence of examples of a single target class.

The model learns to discriminate between the known examples of the positive class and the unknown negative set of counterexamples. The goal is to estimate a function that is positive if an example belongs to a set and negative or zero if the example belongs to the complement of the set.


Note:

Solving a one-class classification problem can be difficult. The accuracy of one-class classifiers cannot usually match the accuracy of standard classifiers built with meaningful counterexamples.

The following topics discuss Anomaly Detection models: