Weighted Naive Bayesian Classifier
Hamad Alhammady · 2007
The naive Bayesian (NB) classifier is one of the simple yet powerful classification methods. One of the important problems in NB (and many other classifiers) is that it is built using crisp classes assigned to the training data. In this paper, we propose an improvement over the NB classifier by employing emerging patterns (EPs) to weight the training instances. That is, we generalize the NB classifier so that it can take into account weighted classes assigned to the training data. EPs are those itemsets whose frequencies in one class are significantly higher than their frequencies in the other classes. Our experiments prove that our proposed method is superior to the original NB classifier.