Dynamic Online Label Distribution Feature Selection Based on Label Importance and Label Correlation Article Swipe
Wanlin Chen
,
Xiao Sun
,
Fuji Ren
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/app15031466
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/app15031466
Existing feature selection methods mainly target single-label learning and multi-label learning, and only a few algorithms are optimized for label distribution learning. In label distribution learning, the associated labels of each sample have different levels of importance. Therefore, multi-label feature selection algorithms cannot be directly applied to label distribution learning. Discretizing label distribution data into multi-label data will cause part of the supervision information to be lost. In most practical applications of label distribution learning, the feature space is undefined, and the features are in the form of flow features. To solve this problem, this paper applies fuzzy rough set theory and applies the flow feature framework to propose a dynamic label distribution feature selection algorithm that handles flow features. Experimental results show that the proposed method is more effective than six state-of-the-art feature selection algorithms on 12 datasets with respect to six representative evaluation metrics.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app15031466
- OA Status
- gold
- Cited By
- 1
- References
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- Related Works
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- OpenAlex ID
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- OpenAlex ID
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https://openalex.org/W4407036860Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/app15031466Digital Object Identifier
- Title
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Dynamic Online Label Distribution Feature Selection Based on Label Importance and Label CorrelationWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-31Full publication date if available
- Authors
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Wanlin Chen, Xiao Sun, Fuji RenList of authors in order
- Landing page
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https://doi.org/10.3390/app15031466Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://doi.org/10.3390/app15031466Direct OA link when available
- Concepts
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Feature selection, Computer science, Artificial intelligence, Multi-label classification, Feature (linguistics), Pattern recognition (psychology), Machine learning, Data mining, Selection (genetic algorithm), Feature vector, Distribution (mathematics), Mathematics, Philosophy, Mathematical analysis, LinguisticsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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65Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.be | 43, 65 |
| abstract_inverted_index.in | 84 |
| abstract_inverted_index.is | 78, 127 |
| abstract_inverted_index.of | 29, 35, 60, 71, 87 |
| abstract_inverted_index.on | 136 |
| abstract_inverted_index.to | 46, 64, 107, 141 |
| abstract_inverted_index.and | 8, 11, 80, 101 |
| abstract_inverted_index.are | 16, 83 |
| abstract_inverted_index.few | 14 |
| abstract_inverted_index.for | 18 |
| abstract_inverted_index.set | 99 |
| abstract_inverted_index.six | 131, 142 |
| abstract_inverted_index.the | 26, 61, 75, 81, 85, 103, 124 |
| abstract_inverted_index.data | 53, 56 |
| abstract_inverted_index.each | 30 |
| abstract_inverted_index.flow | 88, 104, 118 |
| abstract_inverted_index.form | 86 |
| abstract_inverted_index.have | 32 |
| abstract_inverted_index.into | 54 |
| abstract_inverted_index.more | 128 |
| abstract_inverted_index.most | 68 |
| abstract_inverted_index.only | 12 |
| abstract_inverted_index.part | 59 |
| abstract_inverted_index.show | 122 |
| abstract_inverted_index.than | 130 |
| abstract_inverted_index.that | 116, 123 |
| abstract_inverted_index.this | 92, 94 |
| abstract_inverted_index.will | 57 |
| abstract_inverted_index.with | 139 |
| abstract_inverted_index.cause | 58 |
| abstract_inverted_index.fuzzy | 97 |
| abstract_inverted_index.label | 19, 23, 47, 51, 72, 111 |
| abstract_inverted_index.lost. | 66 |
| abstract_inverted_index.paper | 95 |
| abstract_inverted_index.rough | 98 |
| abstract_inverted_index.solve | 91 |
| abstract_inverted_index.space | 77 |
| abstract_inverted_index.cannot | 42 |
| abstract_inverted_index.labels | 28 |
| abstract_inverted_index.levels | 34 |
| abstract_inverted_index.mainly | 4 |
| abstract_inverted_index.method | 126 |
| abstract_inverted_index.sample | 31 |
| abstract_inverted_index.target | 5 |
| abstract_inverted_index.theory | 100 |
| abstract_inverted_index.applied | 45 |
| abstract_inverted_index.applies | 96, 102 |
| abstract_inverted_index.dynamic | 110 |
| abstract_inverted_index.feature | 1, 39, 76, 105, 113, 133 |
| abstract_inverted_index.handles | 117 |
| abstract_inverted_index.methods | 3 |
| abstract_inverted_index.propose | 108 |
| abstract_inverted_index.respect | 140 |
| abstract_inverted_index.results | 121 |
| abstract_inverted_index.Existing | 0 |
| abstract_inverted_index.datasets | 138 |
| abstract_inverted_index.directly | 44 |
| abstract_inverted_index.features | 82 |
| abstract_inverted_index.learning | 7 |
| abstract_inverted_index.metrics. | 145 |
| abstract_inverted_index.problem, | 93 |
| abstract_inverted_index.proposed | 125 |
| abstract_inverted_index.algorithm | 115 |
| abstract_inverted_index.different | 33 |
| abstract_inverted_index.effective | 129 |
| abstract_inverted_index.features. | 89, 119 |
| abstract_inverted_index.framework | 106 |
| abstract_inverted_index.learning, | 10, 25, 74 |
| abstract_inverted_index.learning. | 21, 49 |
| abstract_inverted_index.optimized | 17 |
| abstract_inverted_index.practical | 69 |
| abstract_inverted_index.selection | 2, 40, 114, 134 |
| abstract_inverted_index.Therefore, | 37 |
| abstract_inverted_index.algorithms | 15, 41, 135 |
| abstract_inverted_index.associated | 27 |
| abstract_inverted_index.evaluation | 144 |
| abstract_inverted_index.undefined, | 79 |
| abstract_inverted_index.importance. | 36 |
| abstract_inverted_index.information | 63 |
| abstract_inverted_index.multi-label | 9, 38, 55 |
| abstract_inverted_index.supervision | 62 |
| abstract_inverted_index.Discretizing | 50 |
| abstract_inverted_index.Experimental | 120 |
| abstract_inverted_index.applications | 70 |
| abstract_inverted_index.distribution | 20, 24, 48, 52, 73, 112 |
| abstract_inverted_index.single-label | 6 |
| abstract_inverted_index.representative | 143 |
| abstract_inverted_index.state-of-the-art | 132 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5071943346 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I150229711 |
| citation_normalized_percentile.value | 0.92697019 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |