One-class classification
In machine learning, 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': 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) and many applications can be found in scientific literature, for example outlier detection, anomaly detection, novelty detection.
A similar problem is PU learning, in which a binary classifier is learned in a semi-supervised way from only positive and unlabeled samples.
In PU learning, two sets of samples are assumed to be available for training: the positive set and a mixed set , which is assumed to contain both positive and negative samples, but without these being labeled as such. This contrasts with other forms of semisupervised learning, where it is assumed that a labeled set containing examples of both classes is available in addition to unlabeled samples. A variety of techniques exist to adapt supervised classifiers to the PU learning setting, including variants of the EM algorithm. PU learning has been successfully applied to text, time series, and bioinformatics tasks.
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