Chinese text classification by the Naïve Bayes Classifier and the associative classifier with multiple confidence threshold values

Shing Hwa Lu, Ding An Chiang, Huan Chao Keh, Hui Hua Huang

Research output: Contribution to journalArticlepeer-review

45 Citations (Scopus)

Abstract

Each type of classifier has its own advantages as well as certain shortcomings. In this paper, we take the advantages of the associative classifier and the Naïve Bayes Classifier to make up the shortcomings of each other, thus improving the accuracy of text classification. We will classify the training cases with the Naïve Bayes Classifier and set different confidence threshold values for different class association rules (CARs) to different classes by the obtained classification accuracy rate of the Naïve Bayes Classifier to the classes. Since the accuracy rates of all selected CARs of the class are higher than that obtained by the Naïve Bayes Classifier, we could further optimize the classification result through these selected CARs. Moreover, for those unclassified cases, we will classify them with the Naïve Bayes Classifier. The experimental results show that combining the advantages of these two different classifiers better classification result can be obtained than with a single classifier.

Original languageEnglish
Pages (from-to)598-604
Number of pages7
JournalKnowledge-Based Systems
Volume23
Issue number6
DOIs
Publication statusPublished - Aug 2010
Externally publishedYes

Keywords

  • Association classification
  • Text categorization
  • Text classification
  • Text mining

ASJC Scopus subject areas

  • Software
  • Management Information Systems
  • Information Systems and Management
  • Artificial Intelligence

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