Title:	A NEW SUPERVISED LEARNING ALGORITHM USING NAÏVE BAYESIAN CLASSIFIER
Author(s):	Dewan Md. Farid, Jerome Darmont, and Nouria Harbi, Chowdhury Mofizur Rahman
ISBN:	978-972-8939-09-0
Editors:	Miguel Baptista Nunes, Pedro Isaías and Philip Powell
Year:	2010
Edition:	Single
Keywords:	Conditional probabilities, classification rates, information gain, naïve Bayesian classifier.
Type:	Full Paper
First Page:	78
Last Page:	84
Language:	English
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Paper Abstract:	A new supervised learning algorithm using naïve Bayesian classifier is presented in this paper, which calculates the prior and conditional probabilities from a given training data and classifies the training examples using these probabilities. If any training example is misclassified then the algorithm calculates the information gain of attributes of the training data and chooses one attribute from training data with maximum information gain value. After the algorithm splits the training data into sub-datasets depending on the attribute values of the selected attribute, and again calculates the prior and conditional probabilities for each sub-dataset and classifies the examples of the each sub-dataset using their respective probabilities. The process will continue until all the training examples are correctly classified. Finally, the algorithm preserves the probabilities of each dataset for the future classification of unknown examples, whose attributes value are known but class value is unknown. The proposed algorithm addresses the problem of classifying the large dataset and it has been successfully tested on a number of benchmark problems, which achieved high classification rates using limited computational resources
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