Bayesian network models are widely used for discriminative prediction tasks such as classification. Usually their parameters are determined using 'unsupervised' methods ...
Naive credal classifier 2 (NCC2) extends naive Bayes in order to deliver more robust classifications. NCC2 is based on a set of prior densities rather than on a single prior; as a ...
Class binarizations are effective methods for improving weak learners by decomposing multi-class problems into several two-class problems. This paper analyzes how these methods can...
We describe an in-depth analysis of spam-filtering performance of a simple Naive Bayes learner and two extended variants. A set of seven mailboxes comprising about 65,000 mails f...
In this paper, the naive credal classifier, which is a set-valued counterpart of naive Bayes, is extended to a general and flexible treatment of incomplete data, yielding a new cl...