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https://scholarbank.nus.edu.sg/handle/10635/43156
Title: | Building text classifiers using positive and unlabeled examples | Authors: | Liu, B. Dai, Y. Li, X. Lee, W.S. Yu, P.S. |
Issue Date: | 2003 | Citation: | Liu, B., Dai, Y., Li, X., Lee, W.S., Yu, P.S. (2003). Building text classifiers using positive and unlabeled examples. Proceedings - IEEE International Conference on Data Mining, ICDM : 179-186. ScholarBank@NUS Repository. | Abstract: | This paper studies the problem of building text classifiers using positive and unlabeled examples. The key feature of this problem is that there is no negative example for learning. Recently, a few techniques for solving this problem were proposed in the literature. These techniques are based on the same idea, which builds a classifier in two steps. Each existing technique uses a different method for each step. In this paper, we first introduce some new methods for the two steps, and perform a comprehensive evaluation of all possible combinations of methods of the two steps. We then propose a more principled approach to solving the problem based on a biased formulation of SVM, and show experimentally that it is more accurate than the existing techniques. © 2003 IEEE. | Source Title: | Proceedings - IEEE International Conference on Data Mining, ICDM | URI: | http://scholarbank.nus.edu.sg/handle/10635/43156 | ISBN: | 0769519784 | ISSN: | 15504786 |
Appears in Collections: | Staff Publications |
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