Sparse feature selection via local feature and high-order label correlation

Sun, Lin; Ma, Yuxuan; Ding, Weiping; Xu, Jiucheng

doi:10.1007/s10489-023-05136-9

Appl Intell

2023

DOI: 10.1007/s10489-023-05136-9

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Sparse feature selection via local feature and high-order label correlation

Lin Sun,

Yuxuan Ma,

Weiping Ding

et al.

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2024

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Cited by 6 publications

References 52 publications

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Online streaming feature selection based on hierarchical structure information

Lin,

Wang,

et al. 2024

Concurrency and Computation

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SummaryHierarchical classification learning aims to exploit the hierarchical relationship between data categories. The high dimensionality and dynamic of the data feature space are the main challenges of this research. Hierarchical feature selection uses a hierarchical structure to divide large‐scale tasks into multiple small tasks, which can more effectively improve the training speed and prediction accuracy of classification models. To present, existing online feature selection methods ignore the hierarchical structure of data. In addition, the dependency relationships in the hierarchical structure can serve as auxiliary knowledge to aid feature selection. Based on this, this paper proposes an online streaming hierarchical feature selection method based on kernelized fuzzy rough sets (OFS‐HNFRS). First, we use the prior knowledge of the hierarchical structure to divide the sample set into multiple subsets. Second, the dependency relationship of hierarchical structure is extended to kernelized fuzzy rough sets, and hierarchical category dependency based on kernelized fuzzy rough sets is defined. Finally, a new online feature selection framework is proposed, which is used to evaluate the relevance, significance, and redundancy of features. We verify the effectiveness of the proposed algorithm on six hierarchical datasets and eight flat datasets.

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Online streaming feature selection based on hierarchical structure information

Lin,

Wang,

et al. 2024

Concurrency and Computation

View full text Add to dashboard Cite

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Partial multi-label feature selection based on label matrix decomposition

Liu,

Li,

Yang

et al. 2024

Neural Comput & Applic

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CMEFS: chaotic mapping-based mayfly optimization with fuzzy entropy for feature selection

Sun,

Liang,

Ding

et al. 2024

Appl Intell

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Sparse feature selection via local feature and high-order label correlation

Cited by 6 publications

References 52 publications

Online streaming feature selection based on hierarchical structure information

Online streaming feature selection based on hierarchical structure information

Partial multi-label feature selection based on label matrix decomposition

CMEFS: chaotic mapping-based mayfly optimization with fuzzy entropy for feature selection

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