A new method of parameter estimation for multinomial naive Bayes text classifiers

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Abstract

Multinomial naive Bayes classifies have been widely used for the probabilistic text classification. However, their parameter estimation method sometimes generates inappropriate probabilities. In this paper, we propose a topic document model approach for naive Bayes text classification, where their parameters are estimated with an expectation from the training documents. Experiments are conducted on Reuters 21578 and 20 Newsgroup collection, and our proposed approach obtained a significant improvement in performance over the conventional approach.

Original languageEnglish
Pages (from-to)391-392
Number of pages2
JournalSIGIR Forum (ACM Special Interest Group on Information Retrieval)
Publication statusPublished - 2002 Dec 1

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Parameter estimation
Classifiers
Experiments
Classifier
Text classification
Experiment

ASJC Scopus subject areas

  • Management Information Systems
  • Hardware and Architecture

Cite this

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abstract = "Multinomial naive Bayes classifies have been widely used for the probabilistic text classification. However, their parameter estimation method sometimes generates inappropriate probabilities. In this paper, we propose a topic document model approach for naive Bayes text classification, where their parameters are estimated with an expectation from the training documents. Experiments are conducted on Reuters 21578 and 20 Newsgroup collection, and our proposed approach obtained a significant improvement in performance over the conventional approach.",
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