Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/31921
Title: Is Naïve bayes a good classifier for document classification?
Authors: Ting, SL
Ip, WH 
Tsang, AHC
Keywords: Document classification
Naïve bayes classifier
Text mining
Issue Date: 2011
Source: International journal of software engineering and its applications, 2011, v. 5, no. 3, p. 37-46 How to cite?
Journal: International Journal of Software Engineering and its Applications 
Abstract: Document classification is a growing interest in the research of text mining. Correctly identifying the documents into particular category is still presenting challenge because of large and vast amount of features in the dataset. In regards to the existing classifying approaches, Naïve Bayes is potentially good at serving as a document classification model due to its simplicity. The aim of this paper is to highlight the performance of employing Naïve Bayes in document classification. Results show that Naïve Bayes is the best classifiers against several common classifiers (such as decision tree, neural network, and support vector machines) in term of accuracy and computational efficiency.
URI: http://hdl.handle.net/10397/31921
ISSN: 1738-9984
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