Integrating Global and Local Application of Naive Bayes Classifier

Sotiris Kotsiantis
Department of Mathematics, University of Patras, Greece

 

Abstract: Naive Bayes algorithm captures the assumption that every attribute is independent from the rest of the attributes, given the state of the class attribute. In this study, we attempted to increase the prediction accuracy of the simple Bayes model by integrating global and local application of Naive Bayes classifier. We performed a large-scale comparison with other attempts that have tried to improve the accuracy of the Naive Bayes algorithm as well as other state-of-the-art algorithms on 28 standard benchmark datasets and the proposed method gave better accuracy in most cases.

Keywords: Naive Bayes Classifier, data mining, machine learning.

 

Received July 30, 2011; accepted February 28, 2013