One-class classification

One-class classification

One-class classification tries to distinguish one class of objects from all other possible objects, by learning from a training set 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 [1] and many applications can be found in scientific literature, for example outlier detection, anomaly detection, novelty detection.

See also

References

  1. ^ Moya, M. and Hush, D. (1996). "Network constraints and multi- objective optimization for one-class classification". Neural Networks, 9(3):463–474. doi:10.1016/0893-6080(95)00120-4