The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Multi-Label Classification: An Overview
Abstract
Multi-label classification methods are increasingly required by modern applications, such as protein function classification, music categorization, and semantic scene classification. This article introduces the task of multi-label classification, organizes the sparse related literature into a structured presentation and performs comparative experimental results of certain multilabel classification methods. It also contributes the definition of concepts for the quantification of the multi-label nature of a data set.
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Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
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node2vec: Scalable Feature Learning for Networks
Grover, A., & Leskovec, J. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939754 · Cited by 10,926
A review of classification algorithms for EEG-based brain–computer interfaces: A 10 year update
Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., & Yger, F. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aab2f2 · Cited by 2,239
Anchors: High-Precision Model-Agnostic Explanations
Ribeiro, M. T., Singh, S., & Guestrin, C. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11491 · Cited by 2,162
“Even for a “simpler” output space such as the one present in the multi-label classification setting (Tsoumakas and Katakis 2006), it is not clear if the best option would be to explain each label individually or the set of predicted labels as a single label.”
BackgroundNorouzzadeh, M. S., Nguyen, A., Kosmala, M., Swanson, A., Palmer, M. S., Packer, C., & Clune, J. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1719367115 · Cited by 1,230
Knowledge graph refinement: A survey of approaches and evaluation methods
Paulheim, H. (2016) · Semantic Web · doi:10.3233/sw-160218 · Cited by 849
Metadata
- Authors
- Tsoumakas, G., & Katakis, I.
- Venue
- International Journal of Data Warehousing and Mining
- Publisher
- IGI Global
- Published
- 2007-07-01
- Volume
- 3
- Issue
- 3
- Pages
- 1-13
- ISSN
- 1548-3924 (ISSN-L)
- 1548-3932
- OpenAlex
- W2146241755
- Cited by
- 2,508
- References
- 2
- FWCI
- 55.55
- Type
- journal article
- Language
- ng
- Open access
- closed
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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.
