A Review on Multi-Label Learning Algorithms

By Min-Ling Zhang, Zhi-Hua Zhou. 2014

3,221 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Statistical Learning Theory

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Citations in the corpus

An Overview of Multi-task Learning (2018, neutral): “MTLisrelatedtootherareasinmachinelearn-ing,includingtransferlearning[2],multi-labellearn-ing[3]andmulti-outputregression,butexhibitsdif-ferentcharacteristics.”
A Survey on Multi-Task Learning (2017, background): “MTL is related to other learning paradigms in machine learning, including transfer learning [2], multi-label learning [3] and multi-output regression.”
Transductive Multi-View Zero-Shot Learning (2015, methodology): “Multi-label classification Multi-label classification has been widely studied – for a review of the field please see [32, 34].”
Tencent ML-Images: A Large-Scale Multi-Label Image Database for Visual Representation Learning (2019, methodology): “To evaluate the performance of the trained ResNet-101model with multi-label outputs, we adopt the widely used instancelevel metrics in multi-label learning, including instance-level precision, recall and F1 score [34].”
Learning for Tail Label Data: A Label-Specific Feature Approach (2019, methodology): “• Binary Relevance [Zhang and Zhou, 2014] builds OvR SVM for each label using Liblinear [Fan et al.”
Block-Row Sparse Multiview Multilabel Learning for Image Classification (2016, background): “Here, we follow [39], [40] to categorize the previous multilabel learning”
MEKA: A Multi-label/Multi-target Extension to WEKA (2016, methodology): “…transformation methods as reviewed by Tsoumakas et al. (2010), advanced methods including many of those surveyed by Madjarov et al. (2012) and Zhang and Zhou (2013), many varieties of classifier chains (Read et al., 2011) which have often been used as a benchmark in the recent multi-label…”
Binary relevance for multi-label learning: an overview (2018, methodology): “Here, class labels associated with the training example are regarded to be relevant while their relative importance in characterizing the example’s semantics are not differentiated [1, 2].”
Multi-target regression via input space expansion: treating targets as inputs (2012, background): “Recently, a closely related task called multi-label classification (MLC) (Tsoumakas et al. 2010; Zhang and Zhou 2014) has received increased attention by Machine Learning researchers.”
GOLabeler: Improving Sequence-based Large-scale Protein Function Prediction by Learning to Rank (2017, background): “Automated function prediction is a large-scale multilabel classification problem (Zhang and Zhou, 2014) by regarding one GO term as a class label and also one protein as an instance with multiple labels (multiple GO terms).”
SIN: Semantic Inference Network for Few-Shot Streaming Label Learning (2022, background): “Note that due to the label distribution of multilabel data [62], [63], several labels may correspond to more examples than other labels, however, the comparison of different methods is fair because of the same setting.”
Probabilistic Label Tree for Streaming Multi-Label Learning (2021, background): “Most extant multi-label learning studies focus on a fixed set of labels [1, 13, 20, 26–29, 34, 36, 37].”
Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets (2019, methodology): “For the HoC task, we followed the common practice and reported the example-based F1-score on the abstract level (Zhang and Zhou, 2014; Du et al., 2019).”
Personalized Prescription for Comorbidity (2018, methodology): “We use the common metrics of multi-label, which contains micro under the ROC curve (micro-AUC), macro under the ROC curve (macro-AUC), label ranking average precision score and label ranking loss to promise fair and honest evaluation [31,32].”
The Emerging Trends of Multi-Label Learning (2020, background): “MULTI-LABEL classification (MLC) [1], [2], which assigns multiple labels for each instance simultaneously, is of paramount importance in a variety of fields ranging from protein function classification and document classification, to automatic image categorization.”