Building text classifiers using positive and unlabeled examples

@article{Liu2003BuildingTC,
  title={Building text classifiers using positive and unlabeled examples},
  author={B. Liu and Yang Dai and Xiaoli Li and Wee Sun Lee and Philip S. Yu},
  journal={Third IEEE International Conference on Data Mining},
  year={2003},
  pages={179-186}
}
  • B. Liu, Yang Dai, Philip S. Yu
  • Published 19 November 2003
  • Computer Science
  • Third IEEE International Conference on Data Mining
We study the problem of building text classifiers using positive and unlabeled examples. [] Key Method 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. 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…
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675 Citations
Highly Influential Citations
99
Background Citations
265
Methods Citations
268
Results Citations
15

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675 Citations

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  • Computer Science
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TLDR
This paper has brought about a novel method based on multi-view algorithms for learning from positive and unlabeled examples (LPU) by using the co-EM SVM algorithm, which was previously used for semi-supervised learning.
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