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JCIT
2010

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

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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...
Mohammed J. Islam, Q. M. Jonathan Wu, Majid Ahmadi
Added 19 May 2011
Updated 19 May 2011
Type Journal
Year 2010
Where JCIT
Authors Mohammed J. Islam, Q. M. Jonathan Wu, Majid Ahmadi, Maher A. Sid-Ahmed
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