An Improvement of Accuracy for NaiveBayes by Using Large Word Sets


Lee Jae-Moon, Journal of Information Processing Systems Vol. 7, No. 3, pp. 169-0, Jun. 2006  


Keywords: Accuracy, Machine Learning, NaiveBayes, Document Classification, Large Item Sets
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Abstract

In this paper, we define the large word sets which are noble variations the large item sets in mining association rules, and improve the accuracy for NaiveBayes based on the defined large word sets. In order to use them, a document is divided into the several paragraphs, and then each paragraph can be transformed as the transaction by extracting words in it. The proposed method was implemented by using Al:Categorizer framework and its accuracies were measured by the experiments for reuter-21578 data set. The results of the experiments show that the proposed method improves the accuracy of the conventional NaiveBayes.


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Cite this article
[APA Style]
Jae-Moon, L. (2006). An Improvement of Accuracy for NaiveBayes by Using Large Word Sets. Journal of Internet Computing and Services, 7(3), 169-0.

[IEEE Style]
L. Jae-Moon, "An Improvement of Accuracy for NaiveBayes by Using Large Word Sets," Journal of Internet Computing and Services, vol. 7, no. 3, pp. 169-0, 2006.

[ACM Style]
Lee Jae-Moon. 2006. An Improvement of Accuracy for NaiveBayes by Using Large Word Sets. Journal of Internet Computing and Services, 7, 3, (2006), 169-0.