Weighted Naive Bayesian Classifier Model Based on Information Gain

摘要

Regarding to the disadvantage of Naive Bayesian Classifier (NBC), this paper proposes a new weighted Naive Bayesian Classifier model, which is based on information gain theory (IGWNBC). Using information gain of attribute in attribute set in sample space, we can reduce attribute set, and assign relative weight to each classification attribute. And the result of it is that strengthens attributes, which have high relationship with classification and weakens attributes, which have low relationship with classification. By this way, it can keep Naive Bayesian classifier's easy and effectiveness and improve its classification effect.