One-class classification

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One-class classification, also known as unary classification, tries to identify objects of a specific class amongst all 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. An example is the classification of the operational status of a nuclear plant as 'normal':[1] In this scenario, there are (fortunately) few or no examples of catastrophic system states, only the statistics of normal operation are known. The term One-class classification was coined by Moya & Hush (1996)[2] and many applications can be found in scientific literature, for example outlier detection, anomaly detection, novelty detection.

See also

References

  1. ↑ Tax, D. (2001) One-class classification: Concept-learning in the absence of counter-examples. Doctoral Dissertation, University of Delft, The Netherlands.
  2. ↑ 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


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