Multi-label Learning with Emerging New Labels
2016 IEEE 16th International Conference on Data Mining (ICDM), 2016 Multi-label learning is widely applied in many tasks, where an object possesses multiple concepts with each represented by a class label. Previous studies on multi-label learning have focused on a fixed set of class labels, i.e., the class label set of test data is the same as that in the training set.
Yue Zhu, Kai Ming Ting, Zhi-Hua Zhou
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Identify RNA-associated subcellular localizations based on multi-label learning using Chou's 5-steps rule. [PDF]
BMC Genomics, 2021 Wang H, Ding Y, Tang J, Zou Q, Guo F.
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A Survey of Multi-Label Text Classification Under Few-Shot Scenarios
Applied SciencesMulti-label text classification is a fundamental and important task in natural language processing, with widespread applications in specialized domains such as sentiment analysis, legal document classification, and medical coding.
Wenlong Hu +5 more
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Multi-Label Lifelong Machine Learning: A Scoping Review of Algorithms, Techniques, and Applications
IEEE AccessLifelong machine learning concerns the development of systems that continuously learn from diverse tasks, incorporating new knowledge without forgetting the knowledge they have previously acquired.
Mohammed Awal Kassim +2 more
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Open‐set recognition of compound jamming signal based on multi‐task multi‐label learning
IET Radar, Sonar & NavigationIn the increasingly intricate electromagnetic environment, the radar receiver may simultaneously encounter multiple intentional or unintentional jamming signals, which results in temporal and spectral overlap of received signals and forms a composite ...
Yihan Xiao +3 more
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Automated machine learning for multi-label classification
, 2021 Automated machine learning (AutoML) aims to select and configure machine learning algorithms and combine them into machine learning pipelines tailored to a dataset at hand. For supervised learning tasks, most notably binary and multinomial classification, aka single-label classification (SLC), such AutoML approaches have shown promising results ...
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Use of Chou's 5-steps rule to predict the subcellular localization of gram-negative and gram-positive bacterial proteins by multi-label learning based on gene ontology annotation and profile alignment. [PDF]
J Integr Bioinform, 2020 Bouziane H, Chouarfia A.
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A Multi-Label Learning Framework for Drug Repurposing. [PDF]
Pharmaceutics, 2019 Mei S, Zhang K.
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Adaptive multi-view multi-label learning for identifying disease-associated candidate miRNAs. [PDF]
PLoS Comput Biol, 2019 Liang C, Yu S, Luo J.
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Self-Weighted Multi-Kernel Multi-Label Learning for Potential miRNA-Disease Association Prediction. [PDF]
Mol Ther Nucleic Acids, 2019 Pan Z +6 more
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