Application of improved distributed naive Bayesian algorithms in text classification

The naive Bayes classifier is a widely used text classification method that applies statistical theory to text classification. Due to the particularity of the text, related feature items may generate new semantic information, which may be lost when the traditional vector space model represents text....

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Vydáno v:	The Journal of supercomputing Ročník 75; číslo 9; s. 5831 - 5847
Hlavní autoři:	Gao, Hongyi, Zeng, Xi, Yao, Chunhua
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.09.2019 Springer Nature B.V
Témata:	Algorithms Bayesian analysis Classification Cloud computing Comparative analysis Compilers Computer Science Interpreters Processor Architectures Programming Languages Text editing Websites Feature selection Naive Bayesian algorithm Distributed Text classification
ISSN:	0920-8542, 1573-0484
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Abstract	The naive Bayes classifier is a widely used text classification method that applies statistical theory to text classification. Due to the particularity of the text, related feature items may generate new semantic information, which may be lost when the traditional vector space model represents text. This paper mainly studies the construction and improvement of distributed naive Bayes automatic classification system. The application of Hadoop cloud computing in web page classification is one of the focuses of this article. Firstly, the text classification system and Bayesian classification model are analyzed and discussed, including the representation and extraction of text information, text classification methods and Bayesian text classification methods. Then, in view of the shortcomings of the above-mentioned naive Bayesian text classification method, when training text, we use the mutual information method to check the correlation between the feature sets generated after feature selection, and then combine the features with higher correlation degree appropriately. Through a series of tests, the experimental data show that the improved text classification system can achieve better classification results.
AbstractList	The naive Bayes classifier is a widely used text classification method that applies statistical theory to text classification. Due to the particularity of the text, related feature items may generate new semantic information, which may be lost when the traditional vector space model represents text. This paper mainly studies the construction and improvement of distributed naive Bayes automatic classification system. The application of Hadoop cloud computing in web page classification is one of the focuses of this article. Firstly, the text classification system and Bayesian classification model are analyzed and discussed, including the representation and extraction of text information, text classification methods and Bayesian text classification methods. Then, in view of the shortcomings of the above-mentioned naive Bayesian text classification method, when training text, we use the mutual information method to check the correlation between the feature sets generated after feature selection, and then combine the features with higher correlation degree appropriately. Through a series of tests, the experimental data show that the improved text classification system can achieve better classification results.
Author	Gao, Hongyi Zeng, Xi Yao, Chunhua
Author_xml	– sequence: 1 givenname: Hongyi surname: Gao fullname: Gao, Hongyi email: ghydhr@mail.ustc.edu.cn organization: China Electronics Technology Group Corporation – sequence: 2 givenname: Xi surname: Zeng fullname: Zeng, Xi organization: China Electronics Technology Group Corporation – sequence: 3 givenname: Chunhua surname: Yao fullname: Yao, Chunhua organization: China Electronics Technology Group Corporation
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Cites_doi	10.1016/j.asoc.2016.12.043 10.1016/j.engappai.2016.02.002 10.1109/JSEN.2015.2477540 10.1080/07350015.2014.903086 10.1109/ICDMW.2009.34 10.1007/s10257-014-0252-5 10.1007/s10115-014-0746-y 10.1049/iet-ipr.2017.0892 10.1007/s10618-012-0296-4
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Title	Application of improved distributed naive Bayesian algorithms in text classification
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