Investigating the Performance of Naive- Bayes Classifiers and K- Nearest Neighbor Classifiers

Probability theory is the framework for making decision under uncertainty. In classification, Bayes' rule is used to calculate the probabilities of the classes and it is a big issue how to classify raw data rationally to minimize expected risk. Bayesian theory can roughly be boiled down to one principle: to see the future, one must look at the past. Naive Bayes classifier is one of the mostly used practical Bayesian learning methods. K-Nearest Neighbor is a supervised learning algorithm where the result of new instance query is classified based on majority of K-Nearest Neighbor category. The classifiers do not use any model to fit and only based on memory/ training data. In this paper, after reviewing Bayesian theory the Naive Bayes classifier and KNearest Neighbor classifier is implemented and applied to a dataset "Credit card approval" application. Eventually the performance of these two classifiers is observed on this application in terms of the correct classificatio...

• Type: Journal
• Year: 2010
• Where: JCIT
• Authors: Mohammed J. Islam, Q. M. Jonathan Wu, Majid Ahmadi, Maher A. Sid-Ahmed
• Keywords: JCIT 2010, K-nearest Neighbor, Naive Bayes, Neighbor Classifier