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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/90597

    Title: Chinese text classification by the Naïve Bayes Classifier and the associative classifier with multiple confidence threshold values
    Authors: Lu, Shing-Hwa;Chiang, Ding-An;Keh, Huan-Chao;Huang, Hui-Hua
    Contributors: 淡江大學資訊工程學系
    Keywords: Association classification;Text classification;Text mining;Text categorization
    Date: 2010-08
    Issue Date: 2013-07-03 09:53:33 (UTC+8)
    Publisher: Amsterdam: Elsevier BV
    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.
    Relation: Knowledge-Based Systems 23(6), pp. 598–604
    DOI: 10.1016/j.knosys.2010.04.004
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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