One-class classification tries to distinguish one class of objects from all other possible objects, by learning from a training set

Training set

A training set is a set of data used in various areas of information science to discover potentially predictive relationships. Training sets are used in artificial intelligence, machine learning, genetic programming, intelligent systems, and statistics...

 containing only the objects of that class. This is different from and more difficult than the traditional classification problem, which tries to distinguish between two or more classes with the training set containing objects from all the classes. The term originates from here and many applications can be found in scientific literature, for example outlier detection, anomaly detection

Anomaly detection

Anomaly detection, also referred to as outlier detection refers to detecting patterns in a given data set that do not conform to an established normal behavior....

, novelty detection

Novelty detection

Novelty detection is the identification of new or unknown data or signals that a machine learning system is not aware of during training. Novelty detection is one-class classification. The known data form one class, and a novelty-detection method tries to identify outliers that differ from the...

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