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

Sang Bum Kim, Hae Chang Rim, Heui Seok Lim

Research output: Contribution to journalConference articlepeer-review

7 Citations (Scopus)


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
EventProceedings of the Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval - Tampere, Finland
Duration: 2002 Aug 112002 Aug 15

ASJC Scopus subject areas

  • Management Information Systems
  • Hardware and Architecture


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