NDLI: Analysis of the Use of Background Distribution for Naive Bayes Classifiers

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Analysis of the Use of Background Distribution for Naive Bayes Classifiers

Content Provider	Scilit
Author	Andrade, Daniel Tamura, Akihiro Tsuchida, Masaaki
Copyright Year	2017
Abstract	The naive Bayes classifier is a popular classifier, as it is easy to train, requires no cross-validation for parameter tuning, and can be easily extended due to its generative model. Moreover, recently it was shown that the word probabilities (background distribution) estimated from large unlabeled corpora could be used to improve the parameter estimation of naive Bayes. However, previous methods do not explicitly allow to control how much the background distribution can influence the estimation of naive Bayes parameters. In contrast, we investigate an extension of the graphical model of naive Bayes such that a word is either generated from a background distribution or from a class-specific word distribution. We theoretically analyze this model and show the connection to Jelinek-Mercer smoothing. Experiments using four standard text classification data sets show that the proposed method can statistically significantly outperform previous methods that use the same background distribution.
Related Links	http://www.degruyter.com/downloadpdf/j/jisys.ahead-of-print/jisys-2017-0016/jisys-2017-0016.xml
Ending Page	273
Page Count	15
Starting Page	259
ISSN	03341860
e-ISSN	2191026X
DOI	10.1515/jisys-2017-0016
Journal	Journal of Intelligent Systems
Issue Number	2
Volume Number	28
Language	English
Publisher	Walter de Gruyter GmbH
Publisher Date	2017-07-20
Access Restriction	Open
Subject Keyword	Journal of Intelligent Systems Artificial Intelligence Naive Bayes Semisupervised Classification Empirical Bayes Journal: Journal of Intelligent Systems, Issue- 3-4
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Information Systems Software

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Analysis of the Use of Background Distribution for Naive Bayes Classifiers

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Analysis of the Use of Background Distribution for Naive Bayes Classifiers