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1. Partial Multilabel Learning Using Noise-Tolerant Broad Learning System With Label Enhancement and Dimensionality Reduction NSTL国家科技图书文献中心

Wenbin Qian | Yanqiang Tu ... - 《IEEE transactions on neural networks and learning systems》 - 2025,36(2) - 3758～3772 - 共15页

摘要：Partial multilabel learning (PML) addresses the issue of noisy supervision, which contains an overcomplete set of candidate labels for each instance with only a valid subset of training data. Using la...

关键词： Noise measurement | Learning systems | Dimensionality reduction | Correlation | Sparse matrices | Redundancy | Kernel

2. Label Disambiguation-Based Feature Selection for Partial Multi-label Learning NSTL国家科技图书文献中心

Fankang Xu | Wenbin Qian ... - 《Pattern Recognition,Part VII》 - International Conference on Pattern Recognition - 2025, - 265～279 - 共15页

摘要：Partial multi-label learning (PML) addresses the issue of training a multi-label predictor in the context of inaccurate supervision. Objects in PML are relevant to multiple semantics, but only a subse...

关键词： Feature selection | Partial multi-label learning | Granular ball

3. Multiple reference points-basedmulti-objective feature selection for multi-label learning NSTL国家科技图书文献中心

Yangtao Chen | Wenbin Qian - 《Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies》 - 2024,54(6) - 4952～4978 - 共27页

摘要：In the real world, data often exhibits high-dimensional and complex characteristics. In addition, an object may correspond to multiple class labels. Therefore, filtering and processing such data has b...

关键词： Feature selection | Multi-label learning | Multi-objective optimization | Multiple reference points | Evolutionary algorithm

4. Label distribution feature selection based on label-specific features NSTL国家科技图书文献中心

Wenhao Shu | Qiang Xia ... - 《Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies》 - 2024,54(19) - 9195～9212 - 共18页

摘要：Label distribution learning, where deal with label ambiguity by describing the degree of relevance of each label to a specific instance. As a novel machine learning paradigm, the curse of dimensionali...

关键词： Feature selection | Label-specific features | Feature relevance | Mutual information | Label distribution learning

5. Neighborhood multigranulation rough sets for cost-sensitive feature selection on hybrid data NSTL国家科技图书文献中心

Wenhao Shu | Qiang Xia ... - 《Neurocomputing》 - 2024,565(Jan.14) - 126990.1～126990.15 - 共15页 - 被引量：1

摘要：Feature selection is a vital preprocessing step in real applications of data mining and machine learning. With the prevalence of high-dimensional hybrid data sets in real-world scenarios, along with t...

关键词： Feature selection | Rough sets | Hybrid data | Multi-granulation | Neighborhood | Cost-sensitive learning

6. Incremental feature selection based on uncertainty measure for dynamic interval-valued data NSTL国家科技图书文献中心

Wenhao Shu | Ting Chen ... - 《International journal of machine learning and cybernetics》 - 2024,15(4) - 1453～1472 - 共20页

摘要：Abstract Feature selection is an important strategy for knowledge reduction in rough set. Interval-valued data, as an extension of single values, can better express uncertain information from the pers...

关键词： Feature selection | Rough sets | Interval-valued data | Incremental algorithm | Conditional entropy

7. Multi-label feature selection for missing labels by granular-ball based mutual information NSTL国家科技图书文献中心

Wenhao Shu | Yichen Hu ... - 《Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies》 - 2024,54(23) - 12589～12612 - 共24页

摘要：Multi-label feature selection serves an effective dimensionality reduction technique in the high-dimensional multi-label data. However, most feature selectionmethods regard the label as complete. In f...

关键词： Feature selection | Multi-label learning | Neighborhood rough set | Granular-ball | Missing label

8. Semi-supervised feature selection based on discernibility matrix and mutual information NSTL国家科技图书文献中心

Wenbin Qian | Lijuan Wan ... - 《Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies》 - 2024,54(13/14) - 7278～7295 - 共18页

摘要：Feature selection is a vital technique for reducing data dimensionality. While many granular computing-based feature selection algorithms have been proposed, most have been regarded as a supervised le...

关键词： Feature selection | Rough sets | Granular computing | Indiscernibility measure | Mutual information

9. Neighborhood relation-based incremental label propagation algorithm for partially labeled hybrid data NSTL国家科技图书文献中心

Wenhao Shu | Dongtao Cao ... - 《Machine learning》 - 2024,113(9) - 6293～6339 - 共47页

摘要：Label propagation can rapidly predict the labels of unlabeled objects as the correct answers from a small amount of given label information, which can enhance the performance of subsequent machine lea...

关键词： Partially labeled hybrid data | Neighborhood relation | Probabilistic transition matrix | Incremental label propagation

10. Multi-label feature selection via spectral clustering-based label enhancement and manifold distribution consistency NSTL国家科技图书文献中心

Wenhao Shu | Dongtao Cao ... - 《International journal of machine learning and cybernetics》 - 2024,15(10) - 4669～4693 - 共25页

摘要：Abstract Multi-label feature selection can effectively improve the performance and efficiency of subsequent learning tasks by selecting important features within multi-label data. However, for handlin...

摘要：Partial multilabel learning (PML) addresses the issue of noisy supervision, which contains an overcomplete set of candidate labels for each instance with only a valid subset of training data. Using la...

关键词： Noise measurement | Learning systems | Dimensionality reduction | Correlation | Sparse matrices | Redundancy | Kernel

摘要：Partial multi-label learning (PML) addresses the issue of training a multi-label predictor in the context of inaccurate supervision. Objects in PML are relevant to multiple semantics, but only a subse...

关键词： Feature selection | Partial multi-label learning | Granular ball

摘要：In the real world, data often exhibits high-dimensional and complex characteristics. In addition, an object may correspond to multiple class labels. Therefore, filtering and processing such data has b...

关键词： Feature selection | Multi-label learning | Multi-objective optimization | Multiple reference points | Evolutionary algorithm

摘要：Label distribution learning, where deal with label ambiguity by describing the degree of relevance of each label to a specific instance. As a novel machine learning paradigm, the curse of dimensionali...

关键词： Feature selection | Label-specific features | Feature relevance | Mutual information | Label distribution learning

摘要：Feature selection is a vital preprocessing step in real applications of data mining and machine learning. With the prevalence of high-dimensional hybrid data sets in real-world scenarios, along with t...

关键词： Feature selection | Rough sets | Hybrid data | Multi-granulation | Neighborhood | Cost-sensitive learning

摘要：Abstract Feature selection is an important strategy for knowledge reduction in rough set. Interval-valued data, as an extension of single values, can better express uncertain information from the pers...

关键词： Feature selection | Rough sets | Interval-valued data | Incremental algorithm | Conditional entropy

摘要：Multi-label feature selection serves an effective dimensionality reduction technique in the high-dimensional multi-label data. However, most feature selectionmethods regard the label as complete. In f...

关键词： Feature selection | Multi-label learning | Neighborhood rough set | Granular-ball | Missing label

摘要：Feature selection is a vital technique for reducing data dimensionality. While many granular computing-based feature selection algorithms have been proposed, most have been regarded as a supervised le...

关键词： Feature selection | Rough sets | Granular computing | Indiscernibility measure | Mutual information

摘要：Label propagation can rapidly predict the labels of unlabeled objects as the correct answers from a small amount of given label information, which can enhance the performance of subsequent machine lea...

关键词： Partially labeled hybrid data | Neighborhood relation | Probabilistic transition matrix | Incremental label propagation

摘要：Abstract Multi-label feature selection can effectively improve the performance and efficiency of subsequent learning tasks by selecting important features within multi-label data. However, for handlin...

关键词： Multi-label feature selection | Spectral clustering | Label distribution | Manifold learning

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