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Proceedings of the AAAI Conference on Artificial Intelligence, 2018 In this paper, we propose a new learning framework named dual set multi-label learning, where there are two sets of labels, and an object has one and only one positive label in each set. Compared to general multi-label learning, the exclusive relationship among labels within the same set, and the pairwise inter-set label relationship ...
Chong Liu 0007 +4 more
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Multi-Label Supervised Contrastive Learning
Proceedings of the AAAI Conference on Artificial IntelligenceMulti-label classification is an arduous problem given the complication in label correlation. Whilst sharing a common goal with contrastive learning in utilizing correlations for representation learning, how to better leverage label information remains challenging. Previous endeavors include extracting label-level presentations or mapping labels to an
Pingyue Zhang, Mengyue Wu
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Generative Multi-Label Correlation Learning
ACM Transactions on Knowledge Discovery from Data, 2023In real-world applications, a single instance could have more than one label. To solve this task, multi-label learning methods emerged in recent years. It is a more challenging problem for many reasons, such as complex label correlation, long-tail label distribution, and data shortage.
Lichen Wang +6 more
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Proceedings of the AAAI Conference on Artificial Intelligence, 2018
Embedding methods have shown promising performance in multi-label prediction, as they can discover the dependency of labels. Most embedding methods cannot well align the input and output, which leads to degradation in prediction performance.
Xiaobo Shen 0001 +4 more
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Embedding methods have shown promising performance in multi-label prediction, as they can discover the dependency of labels. Most embedding methods cannot well align the input and output, which leads to degradation in prediction performance.
Xiaobo Shen 0001 +4 more
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Proceedings of the AAAI Conference on Artificial Intelligence, 2018
It is expensive and difficult to precisely annotate objects with multiple labels. Instead, in many real tasks, annotators may roughly assign each object with a set of candidate labels. The candidate set contains at least one but unknown number of ground-truth labels, and is usually adulterated with some irrelevant labels. In this paper,
Ming-Kun Xie, Sheng-Jun Huang
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It is expensive and difficult to precisely annotate objects with multiple labels. Instead, in many real tasks, annotators may roughly assign each object with a set of candidate labels. The candidate set contains at least one but unknown number of ground-truth labels, and is usually adulterated with some irrelevant labels. In this paper,
Ming-Kun Xie, Sheng-Jun Huang
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Multi-Label Learning with Missing Features
2021 International Joint Conference on Neural Networks (IJCNN), 2021Multi-label learning deals with the problem that each example is associated with multiple class labels simultaneously. Existing multi-label learning approaches all assume the feature space is completed and construct classification models using examples with sufficient feature information.
Junlong Li +3 more
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Multi-Directional Multi-Label Learning
Signal Processing, 2021Abstract In multi-label learning, the key problem is to capture the relationships between multiple labels, including proximities and unconformities. In this paper, we consider the relationships among multiple labels from multi-directions, including utilizing discriminative classifier, proposing a general hierarchical constraint and proximity ...
Danyang Wu +4 more
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Discriminative Multi-label Model Reuse for Multi-label Learning
2020Traditional Chinese Medicine (TCM) with diagnosis scales is a holistic way for diagnosing Parkinson’s Disease, where symptoms can be represented as multiple labels. To solve this problem, multi-label learning provides a framework for handling such task and has exhibited excellent performance.
Yi Zhang 0073 +4 more
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Structured feature for multi-label learning
Neurocomputing, 2020Abstract Multi-label learning tackles the problem in which each instance is composed of a single sample and associated with multiple labels simultaneously. In the past decades, many algorithms have been proposed for this emerging machine learning paradigm. However, these methods often focus on designing new classification strategies.
Bo Yang 0041 +4 more
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Multi-Label Learning with PRO Loss
Proceedings of the AAAI Conference on Artificial Intelligence, 2013Multi-label learning methods assign multiple labels to one object. In practice, in addition to differentiating relevant labels from irrelevant ones, it is often desired to rank the relevant labels for an object, whereas the rankings of irrelevant labels are not important.
Xu, Miao, Li, Yu-Feng, Zhou, Zhi-Hua
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