A Naive Bayes Style Possibilistic Classifier

Naive Bayes classifiers can be seen as special probabilistic networks with a star-like structure. They can easily be induced from a dataset of sample cases. However, as most probabilistic approaches, they run into problems, if imprecise (i.e, set-valued) information in the data to learn from has to be taken into account. An approach to handle uncertain as well imprecise information, which recently gained some attention, are possibilistic networks. Because of the close structural resemblance of possibilistic networks to probabilistic ones, the idea suggests itself to construct a possibilistic classifier as a special possibilistic network in much the same way in which a naive Bayes classifier is a special probabilistic network. Thus we obtain a classifier that can easily handle imprecise information in the data to learn from.