The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Building text classifiers using positive and unlabeled examples
Abstract
We study 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. 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.
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
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Estimating the Support of a High-Dimensional Distribution
Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001) · Neural Computation · doi:10.1162/089976601750264965 · Cited by 6,168
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Schölkopf, B., Burges, C. J. C., & Smola, A. J. (1999) · doi:10.5555/299094 · Cited by 5,789
Combining labeled and unlabeled data with co-training
Blum, A., & Mitchell, T. (1998) · Proceedings of the eleventh annual conference on Computational learning theory · doi:10.1145/279943.279962 · Cited by 5,495
Maximum Likelihood from Incomplete Data via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1976) · Cited by 4,517
Making Large-Scale SVM Learning Practical
Joachims, T. (1998) · RePEc: Research Papers in Economics · doi:10.17877/de290r-14262 · Cited by 4,411
A comparison of event models for naive bayes text classification
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Cited by
Editorial: special issue on learning from imbalanced data sets
Chawla, N. V., Japkowicz, N., & Kotcz, A. (2004) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1007730.1007733 · Cited by 2,063
One-Class Collaborative Filtering
Pan, R., Zhou, Y., Cao, B., Liu, N. N., Lukose, R., Scholz, M., & Yang, Q. (2008) · 2008 Eighth IEEE International Conference on Data Mining · doi:10.1109/icdm.2008.16 · Cited by 1,015
Learning from positive and unlabeled data: a survey
Bekker, J., & Davis, J. (2020) · Machine Learning · doi:10.1007/s10994-020-05877-5 · Cited by 651
One-class classification: taxonomy of study and review of techniques
Khan, S. S., & Madden, M. G. (2014) · The Knowledge Engineering Review · doi:10.1017/s026988891300043x · Cited by 600
Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks
You, Q., Luo, J., Jin, H., & Yang, J. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9179 · Cited by 548
Metadata
- Authors
- Liu, B., Dai, Y., Li, X., Lee, W. S., & Yu, P. S.
- Venue
- Third IEEE International Conference on Data Mining
- Publisher
- IEEE Comput. Soc
- Published
- 2004-04-23
- Pages
- 179-186
- ISSN
- 1550-4786 (ISSN-L)
- OpenAlex
- W2134510195
- Cited by
- 667
- References
- 31
- FWCI
- 12.14
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Liu, B., Dai, Y., Li, X., Lee, W. S., & Yu, P. S. (2004). Building text classifiers using positive and unlabeled examples. Third IEEE International Conference on Data Mining. https://doi.org/10.1109/icdm.2003.1250918
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
