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Calibrated Multi-label Classification with Label Correlations
Neural Processing Letters, 2018Multi-label classification is a special learning task where each instance may be associated with multiple labels simultaneously. There are two main challenges: (a) discovering and exploiting the label correlations automatically, and (b) separating the relevant labels from the irrelevant labels of each instance effectively.
Lei Wang, Ming Yang
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Multi-label learning with label-specific features by resolving label correlations
Knowledge-Based Systems, 2018Abstract In multi-label learning, different labels may have their own inherent characteristics for distinguishing each other, in the meanwhile, exploiting the correlations among labels is another practical yet challenging task to improve the performance. In this work, we present a new method for the joint learning of label-specific features and label
Shaozi Li, Song-Zhi Su, Donglin Cao
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Joint multi-label classification and label correlations with missing labels and feature selection
Knowledge-Based Systems, 2019Abstract Multi-label classification problem is a key learning task where each instance may belong to multiple class labels simultaneously. However, there exists four main challenges: (a) designing an effective multi-label classifier, (b) learning the high-order asymmetric label correlations automatically, (c) reducing the dimensionality of feature ...
Yilong Yin
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Asymmetry label correlation for multi-label learning
Applied Intelligence, 2021As an effective method for mining latent information between labels, label correlation is widely adopted by many scholars to model multi-label learning algorithms. Most existing multi-label algorithms usually ignore that the correlation between labels may be asymmetric while asymmetry correlation commonly exists in the real-world scenario.
Jiachao Bao +2 more
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Multi-label weak-label learning via semantic reconstruction and label correlations
Information Sciences, 2023Dawei Zhao, Dong Sun, Qingwei Gao
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Multi-Label Learning by Exploiting Label Correlations Locally
Proceedings of the AAAI Conference on Artificial Intelligence, 2021It is well known that exploiting label correlations is important for multi-label learning. Existing approaches typically exploit label correlations globally, by assuming that the label correlations are shared by all the instances.
Sheng-Jun Huang, Zhi-Hua Zhou
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Multi-label classification by exploiting label correlations
Expert Systems with Applications, 2014Nowadays, multi-label classification methods are of increasing interest in the areas such as text categorization, image annotation and protein function classification. Due to the correlation among the labels, traditional single-label classification methods are not directly applicable to the multi-label classification problem.
Ying Yu +2 more
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Label Distribution Learning by Exploiting Label Correlations
Proceedings of the AAAI Conference on Artificial Intelligence, 2018Label distribution learning (LDL) is a newly arisen machine learning method that has been increasingly studied in recent years. In theory, LDL can be seen as a generalization of multi-label learning. Previous studies have shown that LDL is an effective approach to solve the label ambiguity problem.
Xiuyi Jia +3 more
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Label distribution learning with highâorder label correlations
Concurrency and Computation: Practice and Experience, 2023SummaryLabel distribution learning (LDL) is an emerging learning paradigm, which can be used to solve the label ambiguity problem. In spite of the recent great progress in LDL algorithms considering label correlations, the majority of existing methods only measure pairwise label correlations through the commonly used similarity metric, which is ...
Yulin Li 0002 +4 more
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Multilabel Classification with Label Correlations and Missing Labels
Proceedings of the AAAI Conference on Artificial Intelligence, 2014Many real-world applications involve multilabel classification, in which the labels can have strong inter-dependencies and some of them may even be missing.Existing multilabel algorithms are unable to handle both issues simultaneously.In this paper, we propose a probabilistic model that can automatically learn and exploit multilabel ...
Wei Bi, James T. Kwok
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