Multi-Label Learning
81 papers with code • 1 benchmarks • 8 datasets
Multi-label learning (MLL) is a generalization of the binary and multi-category classification problems and deals with tagging a data instance with several possible class labels simultaneously [1]. Each of the assigned labels conveys a specific semantic relationship with the multi-label data instance [2, 3]. Multi-label learning has continued to receive a lot of research interest due to its practical application in many real-world problems such as recommender systems [4], image annotation [5], and text classification [6].
References:
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Kumar, S., Rastogi, R., Low rank label subspace transformation for multi-label learning with missing labels. Information Sciences 596, 53–72 (2022)
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Zhang M-L, Zhou Z-H (2013) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26(8):1819–1837
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Gibaja E, Ventura S (2015) A tutorial on multilabel learning. ACM Comput Surveys (CSUR) 47(3):1–38
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Bogaert M, Lootens J, Van den Poel D, Ballings M (2019) Evaluating multi-label classifiers and recommender systems in the financial service sector. Eur J Oper Res 279(2):620– 634
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Jing L, Shen C, Yang L, Yu J, Ng MK (2017) Multi-label classification by semi-supervised singular value decomposition. IEEE Trans Image Process 26(10):4612–4625
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Chen Z, Ren J (2021) Multi-label text classification with latent word-wise label information. Appl Intell 51(2):966–979
Datasets
Most implemented papers
Multi-Moments in Time: Learning and Interpreting Models for Multi-Action Video Understanding
Videos capture events that typically contain multiple sequential, and simultaneous, actions even in the span of only a few seconds.
Multi-Label Learning from Single Positive Labels
When the number of potential labels is large, human annotators find it difficult to mention all applicable labels for each training image.
Simple and Robust Loss Design for Multi-Label Learning with Missing Labels
Multi-label learning in the presence of missing labels (MLML) is a challenging problem.
Incomplete Multi-View Weak-Label Learning with Noisy Features and Imbalanced Labels
A variety of modern applications exhibit multi-view multi-label learning, where each sample has multi-view features, and multiple labels are correlated via common views.
DICNet: Deep Instance-Level Contrastive Network for Double Incomplete Multi-View Multi-Label Classification
To deal with the double incomplete multi-view multi-label classification problem, we propose a deep instance-level contrastive network, namely DICNet.
LIFT : Multi-Label Learning with Label-Specific Features
Existing approaches learn from multi-label data by manipulating with identical feature set, i. e. the very instance representation of each example is employed in the discrimination processes of all class labels.
A hybrid algorithm for Bayesian network structure learning with application to multi-label learning
Our extensive experiments show that H2PC outperforms MMHC in terms of goodness of fit to new data and quality of the network structure with respect to the true dependence structure of the data.
LLSF - Learning Label Specific Features for Multi-Label Classifcation
In this paper, we seek to learn label-specific data representation for each class label, which is composed of label-specific features.
Cost-Sensitive Reference Pair Encoding for Multi-Label Learning
Label space expansion for multi-label classification (MLC) is a methodology that encodes the original label vectors to higher dimensional codes before training and decodes the predicted codes back to the label vectors during testing.
Semi-supervised multi-label feature selection via label correlation analysis with l1-norm graph embedding
Compared with the previous works, there are two advantages of our algorithm: (1) Manifold learning which leverages the underlying geometric structure of the training data is imposed to utilize both labeled and unlabeled data.