WNB: A Weighted Naïve Bayesian Classifier

The naive Bayes classifier (NB) aims at classifying a given instance into a discrete class considering that all attributes are conditionally independent given the class. NB has been extensively used for modeling knowledge in many different applications and has been the focus of many works related to classification tasks. This work proposes and discusses a Naive Bayesian classifier named weighted naive Bayesian (WNB) classifier. The central idea of WNB is that more relevant attributes should have more influence in the classification estimation process. A weighting strategy is adopted to modify the traditional NB. Experiments performed with six UCI domains show that WNB is promising.