Experimental study on generalization capability of extended naive Bayesian classifier

Extended naive Bayesian classifier (ENBC) is a general framework of NBCs, which is developed based on \(t\)-norm based ordered weighted averaging (\(t\)-OWA) operator and uses the weighted summation of products of margin probabilities to determine class-conditional probability. Since ENBC was proposed in 2006, there is no such a study which tests the performances of ENBC on the real classification datasets. Thus, in this paper we conduct an experimental investigation to ENBC’s generalization capability based on 44 benchmark KEEL and UCI datasets. The analysis shows that (1) ENBC is instable and its aggregation weights are sensitive to the order of training samples and (2) ENBC indeed has higher generalization capability than the existing NBCs, e.g., normal naive Bayesian and flexible naive Bayesian when its weight is properly selected.