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Robust Multi-Label Classification with Enhanced Global and Local Label Correlation
Mathematics, 2022 Data representation is of significant importance in minimizing multi-label ambiguity. While most researchers intensively investigate label correlation, the research on enhancing model robustness is preliminary. Low-quality data is one of the main reasons
Tianna Zhao +2 more
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Nearest labelset using double distances for multi-label classification [PDF]
PeerJ Computer Science, 2019 Multi-label classification is a type of supervised learning where an instance may belong to multiple labels simultaneously. Predicting each label independently has been criticized for not exploiting any correlation between labels.
Hyukjun Gweon +2 more
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Association Rules-Based Classifier Chains Method
IEEE Access, 2022 The order for label learning is very important to the classifier chains method, and improper order can limit learning performance and make the model very random.
Ding Jiaman +4 more
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Neighbor-Based Label Distribution Learning to Model Label Ambiguity for Aerial Scene Classification
Remote Sensing, 2021 Many aerial images with similar appearances have different but correlated scene labels, which causes the label ambiguity. Label distribution learning (LDL) can express label ambiguity by giving each sample a label distribution. Thus, a sample contributes
Jianqiao Luo +4 more
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Exploring Common and Label-Specific Features for Multi-Label Learning With Local Label Correlations
IEEE Access, 2020 In multi-label learning, instances can be associated with a set of class labels. The existing multi-label feature selection (MLFS) methods generally adopt either of these two strategies, namely, selecting a subset of features that is shared by all labels
Yunzhi Ling +3 more
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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
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An Effective Multi-Label Feature Selection Model Towards Eliminating Noisy Features
Applied Sciences, 2020 Feature selection has devoted a consistently great amount of effort to dimension reduction for various machine learning tasks. Existing feature selection models focus on selecting the most discriminative features for learning targets.
Jun Wang +5 more
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Journal of King Saud University: Computer and Information Sciences, 2022 Label distribution learning is a novel machine learning paradigm to deal with label ambiguity, which is the generalization of the traditional single-label learning and multi-label learning paradigms. Though label distribution learning has attracted a lot
Ruiqi Guo +3 more
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Label distribution learning via label correlation grid
CoRR, 2022 Label distribution learning can characterize the polysemy of an instance through label distributions. However, some noise and uncertainty may be introduced into the label space when processing label distribution data due to artificial or environmental factors.
Qimeng Guo +3 more
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Semi-Supervised Multi-Label Dimensionality Reduction Learning by Instance and Label Correlations
Mathematics, 2023 The label learning mechanism is challenging to integrate into the training model of the multi-label feature space dimensionality reduction problem, making the current multi-label dimensionality reduction methods primarily supervision modes.
Runxin Li +5 more
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