A Tutorial on Multilabel Learning
By E. Gibaja Galindo, Sebastián Ventura. 2015
487 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Science Applications
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Francisco Herrera
- Salvador Garcia
- Xia Hu
- Qingquan Song
- Julián Luengo
- José Manuel Benítez
- Eric P. Xing
- Qiang Yang
In Computer Science Applications
Citations in the corpus
- Large-Scale Hierarchical Text Classification with Recursively Regularized Deep Graph-CNN (2018, background): “For large scale hierarchical text classification, there have been many existing studies to design better output cost functions [12, 46].”
- Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms (2020, methodology): “The first two metrics have appeared in some prior FL work, while the latter two are the primary evaluation metrics typically used in multi-label classification work [Gibaja and Ventura, 2015].”
- Big data preprocessing: methods and prospects (2016, background): “This is the case of ordinal classification/regression [111], multi-label classification [52, 53, 112] or multi-instance learning [54].”
- Multi-label Adversarial Perturbations (2018, background): “ification, multilabel ranking is a more general task aiming to generate a consistent ranking among labels and could be transferred into the multi-label classification grounded on bipartition thresholds [49]. Besides traditional ranking-based approaches [55], [56], recent advances have also combined mu”
- Binary relevance for multi-label learning: an overview (2018, background): “A number of popular multi-label evaluation metrics have been proposed [1,2,10,11].”
- Online Metric Learning for Multi-Label Classification (2020, background): “In the development of multi-label classification (Tsoumakas, Zhang, and Zhou 2012; Gibaja and Ventura 2015), one challenge that remains unsolved is that most multi-label classification algorithms are developed in an off-line mode (Cheng and Hüllermeier 2009; Chen and Lin 2012; Babbar and Schölkopf 2”