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Design and Development of Naive Bayes Classifier

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dc.description.abstract The naïve Bayes classifier is a simple form of Bayesian classifiers which assumes all the features are independent of each other. Despite this assumption, the naïve Bayes classifier’s accuracy is comparable to other sophisticated classifiers. In this paper we designed and developed a naïve Bayes classifier for a better understanding of the algorithm. The classifier is tested on two different data sets from the University of California at Irvine machine learning repository. Different cross validation methods are used to calculate the accuracy of the developed classifier. The different accuracies obtained are compared to get the best accuracy of the classifier. This value is also compared with accuracies obtained for the same data sets using different algorithms reported in other papers. It was observed from the comparisons that the naïve Bayes classifier’s results are very comparable to other algorithms. en_US
dc.title Design and Development of Naive Bayes Classifier en_US
dc.date.accessioned 2013-07-22T14:00:10Z
dc.date.available 2013-07-22T14:00:10Z
dc.date.issued 2013-07-22
dc.identifier.uri http://hdl.handle.net/10365/23048
dc.thesis.degree Paper (M.S.)--North Dakota State University, 2013. en_US
dc.contributor.advisor Nygard, Kendall
dc.subject.lcsh Bayesian statistical decision theory. en_US
dc.subject.lcsh Automatic classification.
dc.subject.lcsh Machine learning.
dc.creator.author Garg, Bandana
dc.degree.departmentCollege Master of Science / Computer Science, College of Science and Mathematics, 2013.
dc.date.created 2013
dc.date.created 2013

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