Please use this identifier to cite or link to this item:
|Title:||Building text classifiers using positive and unlabeled examples|
|Source:||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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 9, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.