The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Large scale multi-label classification via metalabeler
Abstract
The explosion of online content has made the management of such content non-trivial. Web-related tasks such as web page categorization, news filtering, query categorization, tag recommendation, etc. often involve the construction of multi-label categorization systems on a large scale. Existing multi-label classification methods either do not scale or have unsatisfactory performance. In this work, we propose MetaLabeler to automatically determine the relevant set of labels for each instance without intensive human involvement or expensive cross-validation. Extensive experiments conducted on benchmark data show that the MetaLabeler tends to outperform existing methods. Moreover, MetaLabeler scales to millions of multi-labeled instances and can be deployed easily. This enables us to apply the MetaLabeler to a large scale query categorization problem in Yahoo!, yielding a significant improvement in performance.
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Cites
The class imbalance problem: A systematic study1
Japkowicz, N., & Stephen, S. (2002) · Intelligent Data Analysis · doi:10.3233/ida-2002-6504 · Cited by 3,295
Transforming classifier scores into accurate multiclass probability estimates
Zadrozny, B., & Elkan, C. (2002) · Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/775047.775151 · Cited by 1,280
Cited by
A Review on Multi-Label Learning Algorithms
Zhang, M.-L., & Zhou, Z.-H. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.39 · Cited by 3,321
Relational learning via latent social dimensions
Tang, L., & Liu, H. (2009) · Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1557019.1557109 · Cited by 799
Self-citationTsatsaronis, G., Balikas, G., Malakasiotis, P., Partalas, I., Zschunke, M., Alvers, M. R., Weissenborn, D., Krithara, A., Petridis, S., Polychronopoulos, D., et al. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0564-6 · Cited by 598
A Tutorial on Multilabel Learning
Gibaja, E., & Ventura, S. (2015) · ACM Computing Surveys · doi:10.1145/2716262 · Cited by 527
Representation Learning for Attributed Multiplex Heterogeneous Network
Cen, Y., Zou, X., Zhang, J., Yang, H., Zhou, J., & Tang, J. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330964 · Cited by 465
Metadata
- Authors
- Tang, L., Rajan, S., & Narayanan, V. K.
- Venue
- Proceedings of the 18th international conference on World wide web
- Publisher
- ACM
- Published
- 2009-04-20
- Pages
- 211-220
- OpenAlex
- W2129144539
- Cited by
- 191
- References
- 2
- FWCI
- 9.40
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Tang, L., Rajan, S., & Narayanan, V. K. (2009). Large scale multi-label classification via metalabeler. Proceedings of the 18th international conference on World wide web. https://doi.org/10.1145/1526709.1526738
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