The DOI matches its Crossref record; 120 of the 120 references it cites were found in a registry or an index.
A Tutorial on Multilabel Learning
Abstract
Multilabel learning has become a relevant learning paradigm in the past years due to the increasing number of fields where it can be applied and also to the emerging number of techniques that are being developed. This article presents an up-to-date tutorial about multilabel learning that introduces the paradigm and describes the main contributions developed. Evaluation measures, fields of application, trending topics, and resources are also presented.
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
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
(1992) · Cited by 23,431
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 20,825
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
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
Lafferty, J., McCallum, A., & Pereira, F. C. N. (2001) · ScholarlyCommons (University of Pennsylvania) · Cited by 12,935
Scikit-learn: Machine Learning in Python
(2011) · Journal of Machine Learning Research · doi:10.5555/1953048.2078195 · Cited by 8,539
Wolpert, D. H. (1992) · Neural Networks · doi:10.1016/s0893-6080(05)80023-1 · Cited by 7,951
Machine learning in automated text categorization
Sebastiani, F. (2002) · ACM Computing Surveys · doi:10.1145/505282.505283 · Cited by 7,926
Cited by
Big data preprocessing: methods and prospects
García, S., Ramírez-Gallego, S., Luengo, J., Benítez, J. M., & Herrera, F. (2016) · Big Data Analytics · doi:10.1186/s41044-016-0014-0 · Cited by 709
A review of methods for imbalanced multi-label classification
Tarekegn, A. N., Giacobini, M., & Michalak, K. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.107965 · Cited by 426
Suri, J. S., Bhagawati, M., Paul, S., Protogerou, A. D., Sfikakis, P. P., Kitas, G. D., Khanna, N. N., Ruzsa, Z., Sharma, A. M., Saxena, S., et al. (2022) · Diagnostics · doi:10.3390/diagnostics12030722 · Cited by 55
Kaur, R., Ginige, J. A., & Obst, O. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118997 · Cited by 48
(2018) · Europe PMC (PubMed Central) · Cited by 30
Metadata
- Authors
- Gibaja, E., & Ventura, S.
- Venue
- ACM Computing Surveys
- Published
- 2015-04-16
- Volume
- 47
- Issue
- 3
- Pages
- 1-38
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2029517229
- Cited by
- 527
- References
- 120
- FWCI
- 40.98
- Type
- journal article
- Language
- en
- 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.
