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Label Distribution Learning with Label Correlations on Local Samples
IEEE Transactions on Knowledge and Data Engineering, 2021Label distribution learning (LDL) is proposed for solving the label ambiguity problem in recent years, which can be seen as an extension of multi-label learning. To improve the performance of label distribution learning, some existing algorithms exploit label correlations in a global manner that assumes the label correlations are shared by all ...
Xiuyi Jia +4 more
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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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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.
He, Zhi-Fen +3 more
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Label distribution learning with high‐order 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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Multi-label Learning by Exploiting Imbalanced Label Correlations
2021Multi-label classification refers to the supervised learning problem where an instance may be associated with multiple labels. It is well known that exploiting label correlations is important for multi-label learning. Existing approaches typically assume that the distribution of classes is balanced. In many real-world applications, multi-label datasets
Shiqiao Gu +3 more
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An Multi-Label Classification with Label Correlation
Asian Journal of Research in Social Sciences and Humanities, 2016Nowadays multi-label data are numerously available in real-world applications. Multi-label data instances are associated with more number of class labels at same time. Generally, the multi-label classification is done in many ways. Recognize of label correlation in multi-label data is difficult.
S. Sabena +3 more
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Exploring Label Correlations for Partitioning the Label Space in Multi-label Classification
2021 International Joint Conference on Neural Networks (IJCNN), 2021Recent works on Multi-Label Classification (MLC) present multiple strategies to explore label correlations in a way to improve classifiers performances. However, these works focus only in the traditional local and global approaches, i.e., transforming the original problem into a set of binary local problems, or dealing globally with all classes ...
Elaine Cecília Gatto +2 more
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Correlation Clustering with Stochastic Labellings
2013Correlation clustering is the problem of finding a crisp partition of the vertices of a correlation graph in such a way as to minimize the disagreements in the cluster assignments. In this paper, we discuss a relaxation to the original problem setting which allows probabilistic assignments of vertices to labels. By so doing, overlapping clusters can be
Nicola Rebagliati +2 more
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