Improving the Classification Accuracy Using Unlabeled Data: A Naive Bayesian Case


KIPS Transactions on Software and Data Engineering, Vol. 13, No. 4, pp. 457-462, Apr. 2006
10.3745/KIPSTB.2006.13.4.457, Full Text:

Abstract

In many applications, an enormous amount of unlabeled data is available with little cost. Therefore, it is natural to ask whether we can take advantage of these unlabeled data in classification learning. In this paper, we analyzed the role of unlabeled data in the context of naive Bayesian learning. Experimental results show that including unlabeled data as part of training data can significantly improve the performance of classification accuracy. The effect of using unlabeled data is especially important in case labeled data are sparse.


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Cite this article
[IEEE Style]
C. H. Lee, "Improving the Classification Accuracy Using Unlabeled Data: A Naive Bayesian Case," KIPS Journal B (2001 ~ 2012) , vol. 13, no. 4, pp. 457-462, 2006. DOI: 10.3745/KIPSTB.2006.13.4.457.

[ACM Style]
Chang Hwan Lee. 2006. Improving the Classification Accuracy Using Unlabeled Data: A Naive Bayesian Case. KIPS Journal B (2001 ~ 2012) , 13, 4, (2006), 457-462. DOI: 10.3745/KIPSTB.2006.13.4.457.