Bayesian networks for one-class classification

Question

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.

And:

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.

Answer 1

Quite simple, yet actionable approach:

• Collect your data, preprocess them to get categorical features $X$.
• Create, tune, optimize the Bayesian network for $X$ with bnlearn. As a result we have practically a probability distribution $p(X)$.
• Take all your observations and calculate their likelihoods $L_i=p(x_i)$.
• Based on the likelihoods define a threshold $\theta$ for false negatives, i.e. if the desired sensitivity is e.g. 95%, you should take the likelihood that corresponds to the 5th quantile.

The resulting classifier is then: $p(X)>\theta$.

The trick is that you never know how similar are the unobserved counter examples. However, based on some ex-post observations, you can tune the threshold also with the respect to specificity.