Granular Multilabel Batch Active Learning With Pairwise Label Correlation
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- Type
- article
- Published
- 2022-05-01
- Cited by
- 21
- References
- 50
- OpenAlex
- https://openalex.org/W3136584210
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:234224415
Keywords
Pairwise comparison, Active learning (machine learning), Correlation, Artificial intelligence, Computer science
References
- HCP: A Flexible CNN Framework for Multi-Label Image Classification
- Multilabel Image Classification Via High-Order Label Correlation Driven Active Learning
- Optimal batch selection for active learning in multi-label classification
- Querying discriminative and representative samples for batch mode active learning
- ON THE LAWS OF INHERITANCE IN MAN I. INHERITANCE OF PHYSICAL CHARACTERS
- A method based on the chi-square test for document classification
- Active Learning by Querying Informative and Representative Examples
- A survey on instance selection for active learning
- A Tutorial on Multilabel Learning
- Diverse Expected Gradient Active Learning for Relative Attributes
- A Batch-Mode Active Learning Technique Based on Multiple Uncertainty for SVM Classifier
- Granular Computing: Perspectives and Challenges
- Lift: Multi-Label Learning with Label-Specific Features
- Active Learning for Domain Adaptation in the Supervised Classification of Remote Sensing Images
- A Review on Multi-Label Learning Algorithms
- Batch-Mode Active-Learning Methods for the Interactive Classification of Remote Sensing Images
- Multilabel SVM active learning for image classification
- Batch Mode Active Learning with Applications to Text Categorization and Image Retrieval
- Active subspace learning
- Effective multi-label active learning for text classification
Cited by
- Learning label-specific features with global and local label correlation for multi-label classification
- Selective label enhancement for multi-label classification based on three-way decisions
- Three-way neighborhood based stream computing for incomplete hybrid information system
- Incremental approaches for optimal scale selection in dynamic multi-scale set-valued decision tables
- BGRF: A broad granular random forest algorithm
- A meta-framework for multi-label active learning based on deep reinforcement learning
- Multiview Multilabel Classification With Group-Based Feature and Label Selection
- Multi-granular labels with three-way decisions for multi-label classification
- A survey on multi-label feature selection from perspectives of label fusion
- Latent Topic-Aware Multioutput Learning
- Batch-Mode Active Learning of Gaussian Process Regression With Maximum Model Change
- FIG: Feature-Weighted Information Granules With High Consistency Rate
- A Progressive Stacking Pseudoinverse Learning Framework via Active Learning in Random Subspaces
- Clustering Environment Aware Learning for Active Domain Adaptation
- Granular-Balls based Fuzzy Twin Support Vector Machine for Classification
- Multi-Label Bayesian Active Learning with Inter-Label Relationships
- Semi-supervised batch active learning based on mutual information
- Three-way multi-label classification: A review, a framework, and new challenges
- Fine-grained local label correlation for multi-label classification
- Dual-granularity multi-instance multi-label learning with variational autoencoder
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