DocumentCode :
2627731
Title :
Investigating the Performance of Naive- Bayes Classifiers and K- Nearest Neighbor Classifiers
Author :
Islam, Mohammed J. ; Wu, Q. M Jonathan ; Ahmadi, Majid ; Sid-Ahmed, Maher A.
Author_Institution :
Univ. of Windsor, Windsor
fYear :
2007
fDate :
21-23 Nov. 2007
Firstpage :
1541
Lastpage :
1546
Abstract :
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 k-nearest 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 classification and misclassification and how the performance of k-nearest neighbor classifier can be improved by varying the value of k.
Keywords :
Bayes methods; credit transactions; decision making; learning (artificial intelligence); pattern classification; query processing; statistical analysis; Bayes rule; Bayesian learning method; credit card approval; decision making; instance query; k-nearest neighbor classifiers; naive-Bayes classifiers; probability theory; supervised learning algorithm; Bayesian methods; Credit cards; Learning systems; Machine learning; Machine learning algorithms; Nearest neighbor searches; Neural networks; Pattern recognition; Probability; Training data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Convergence Information Technology, 2007. International Conference on
Conference_Location :
Gyeongju
Print_ISBN :
0-7695-3038-9
Type :
conf
DOI :
10.1109/ICCIT.2007.148
Filename :
4420473
Link To Document :
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