The DOI matches its Crossref record.
Collective multi-label classification
Abstract
Common approaches to multi-label classification learn independent classifiers for each category, and employ ranking or thresholding schemes for classification. Because they do not exploit dependencies between labels, such techniques are only well-suited to problems in which categories are independent. However, in many domains labels are highly interdependent. This paper explores multi-label conditional random field (CRF)classification models that directly parameterize label co-occurrences in multi-label classification. Experiments show that the models outperform their single-label counterparts on standard text corpora. Even when multi-labels are sparse, the models improve subset classification error by as much as 40%.
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It's who you know: graph mining using recursive structural features
Henderson, K., Gallagher, B., Li, L., Akoglu, L., Eliassi-Rad, T., Tong, H., & Faloutsos, C. (2011) · Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2020408.2020512 · Cited by 223
Metadata
- Authors
- Ghamrawi, N., & McCallum, A.
- Venue
- Proceedings of the 14th ACM international conference on Information and knowledge management
- Publisher
- ACM
- Published
- 2005-10-31
- Pages
- 195-200
- OpenAlex
- W1998839399
- Cited by
- 410
- References
- 0
- FWCI
- 5.96
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Ghamrawi, N., & McCallum, A. (2005). Collective multi-label classification. Proceedings of the 14th ACM international conference on Information and knowledge management. https://doi.org/10.1145/1099554.1099591
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
