Improving the Accuracy of Text Classification by the Different Classifier with Multiple Confidence Threshold Values
博士 === 淡江大學 === 資訊工程學系博士班 === 98 === 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 Naive Bayes classifier to make up the shortcomings of each other, thus improving the accuracy of text class...
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| Other Authors: | |
| Format: | Others |
| Language: | en_US |
| Published: |
2010
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| Online Access: | http://ndltd.ncl.edu.tw/handle/79918901302205738563 |
| Summary: | 博士 === 淡江大學 === 資訊工程學系博士班 === 98 === 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 Naive 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 Naive 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 Naive Bayes classifier to the classes. Since the accuracy rates of all selected CARs of the class are higher than that obtained by the Naive Bayes classifier, we could further optimize the classification result through these selected CARs. Moreover, for those unclassified cases, we will classify them with the Naive 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.
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