Naïve possibilistic network classifiers

Naïve Bayesian network classifiers have proved their effectiveness to accomplish the classification task, even if they work under the strong assumption of independence of attributes in the context of the class node. However, as all of them are based on probability theory, they run into problems when they are faced with imperfection. This paper proposes a new approach of classification under the possibilistic framework with naïve classifiers. To output the naïve possibilistic network classifier, two procedures are studied namely the building phase, which deals with imperfect (imprecise/uncertain) dataset attributes and classes, and the classification phase, which is used to classify new instances that may be characterized by imperfect attributes. To improve the performance of our classifier, we propose two extensions namely selective naïve possibilistic classifier and semi-naïve possibilistic classifier. Experimental study has shown naïve Bayes style possibilistic classifier, and is efficient in the imperfect case.