From the definition of one-class classification in wikipedia:
In machine learning, one-class classification, also known as unary classification or class-modelling, tries to identify objects of a specific class amongst all objects, by learning from a training set containing only the objects of that class.
A similar problem is PU learning, in which a binary classifier is learned in a semi-supervised way from only positive and unlabeled sample points.
I'm looking for examples of this in the context of Bayesian Networks. I imagine a case in which for one node of the network there are only positive examples available, but a binary classifier is desired. I want to know of any examples of such a case, if possible with associated code. I'm mostly using the bnlearn package in R but I could use anything.
Note: although this has been used extensively for outlier detection, the problem I would like to tackle isn't really an outlier detection one. I want to model animal species distribution and I only have positive examples: places where an animal species was observed. But naturally cannot be sure of the species absence if it wasn't observed during a certain sampling effort.