SNNB: A Selective Neighborhood Based Naïve Bayes for Lazy Learning

Naive Bayes is a probability-based classification method which is based on the assumption that attributes are conditionally mutually independent given the class label. Much research has been focused on improving the accuracy of Naïve Bayes via eager learning. In this paper, we propose a novel lazy learning algorithm, Selective Neighbourhood based Naïve Bayes (SNNB). SNNB computes different distance neighborhoods of the input new object, lazily learns multiple Naïve Bayes classifiers, and uses the classifier with the highest estimated accuracy to make decision. The results of our experiments on 26 datasets show that our proposed SNNB algorithm outperforms Naïve Bayes, and state-of-the-art classification methods NBTree, CBA, and C4.5 in terms of accuracy as well as efficiency.