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 will be positive if an example belongs to a set and negative or zero if the example belongs to the complement of the set. This type of problem is called anomaly detection.
One-class SVM models are useful in situations where you wish to detect atypical or anomalous cases, that is, cases that don't fit with the majority of cases. For example, you might want to predict people likely to purchase a product when you have detailed information about people who did purchase the product and have little or no information about people who did not purchase the product.
One-class SVM models, unlike SVM classification and regression models, do not have a target.
One-class SVM models are useful also in cases where it is difficult to provide counterexamples. For example, in text document classification, it is easy to classify a document under a given topic. However, the universe of documents not belonging to this topic can be very large and it may not be feasible to provide counterexamples.
SVM is the only Oracle Data mining algorithm that can operate in one-class mode.
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