Multi-Label Feature Selection Based on High-Order Label Correlation Assumption [PDF]
Entropy, 2020 Multi-label data often involve features with high dimensionality and complicated label correlations, resulting in a great challenge for multi-label learning.
Ping Zhang +3 more
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Partial Classifier Chains with Feature Selection by Exploiting Label Correlation in Multi-Label Classification [PDF]
Entropy, 2020 Multi-label classification (MLC) is a supervised learning problem where an object is naturally associated with multiple concepts because it can be described from various dimensions.
Zhenwu Wang +3 more
doaj +2 more sources
Multi-Label Learning with Global and Local Label Correlation [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2018 It is well-known that exploiting label correlations is important to multi-label learning. Existing approaches either assume that the label correlations are global and shared by all instances; or that the label correlations are local and shared only by a data subset.
Yue Zhu +2 more
exaly +4 more sources
Multi-Label Learning With Label Specific Features Using Correlation Information
IEEE Access, 2019 To deal with the problem where each instance is associated with multiple labels, a lot of multi-label learning algorithms have been developed in recent years.
Huirui Han +4 more
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Research on plug-and-play correlation enhancement modules in deep multi-label learning [PDF]
Scientific ReportsExtreme Multi-Label Text Classification (XMTC) is a crucial task in natural language processing, aiming to assign the most relevant subset of labels to an input text from an extremely large label set.
Jiliang Zhang, Chunhong Yuan, Xiangyu Li
doaj +2 more sources
Improving Multi-Label Learning by Correlation Embedding
Applied Sciences, 2021 In multi-label learning, each object is represented by a single instance and is associated with more than one class labels, where the labels might be correlated with each other.
Jun Huang +4 more
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Multilabel Remote Sensing Image Annotation With Multiscale Attention and Label Correlation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Deep-learning-based multilabel image annotation is receiving increasing attention in the field of remote sensing due to the great success of deep networks in single-label remote sensing image classification.
Rui Huang, Fengcai Zheng, Wei Huang
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S-MAT: Semantic-Driven Masked Attention Transformer for Multi-Label Aerial Image Classification
Sensors, 2022 Multi-label aerial scene image classification is a long-standing and challenging research problem in the remote sensing field. As land cover objects usually co-exist in an aerial scene image, modeling label dependencies is a compelling approach to ...
Hongjun Wu, Cheng Xu, Hongzhe Liu
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MLGN:A Multi-Label Guided Network for Improving Text Classification
IEEE Access, 2023 Within natural language processing, multi-label classification is an important but challenging task. It is more complex than single-label classification since the document representations need to cover fine-grained label information, while the labels ...
Qiang Liu +6 more
doaj +1 more source
Generalized Label Enhancement with Sample Correlations [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2022 Recently, label distribution learning (LDL) has drawn much attention in machine learning, where LDL model is learned from labelel instances. Different from single-label and multi-label annotations, label distributions describe the instance by multiple labels with different intensities and accommodate to more general scenes.
Qinghai Zheng +5 more
openaire +2 more sources