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http://dx.doi.org/10.3745/KIPSTB.2006.13B.4.457

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

Lee Chang-Hwan (동국대학교 정보통신학과)
Publication Information
The KIPS Transactions:PartB / v.13B, no.4, 2006 , pp. 457-462 More about this Journal
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.
Keywords
Machine Learning; Semi-supervised Learning; Artificial Intelligence;
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