NSTL国家科技图书文献中心

1. GBRS: A Unified Granular-Ball Learning Model of Pawlak Rough Set and Neighborhood Rough Set NSTL国家科技图书文献中心

Shuyin Xia | Cheng Wang ... - 《IEEE transactions on neural networks and learning systems》 - 2025,36(1) - 1719～1733 - 共15页

摘要： (GBRS) based on the granular-ball* computing combining* | deficit, this article presents a granular-ball* rough set* | -ball* computing. The GBRS can simultaneously represent* | the robustness and the adaptability of the granular | Pawlak rough set (PRS) and neighborhood rough

关键词： Rough sets | Feature extraction | Filtering theory | Computational modeling | Filtering algorithms | Classification algorithms | Training

2. Fuzzy Granule Density-Based Outlier Detection With Multi-Scale Granular Balls NSTL国家科技图书文献中心

Can Gao | Xiaofeng Tan ... - 《IEEE Transactions on Knowledge and Data Engineering》 - 2025,37(3) - 1182～1197 - 共16页

摘要： based on granular-ball* computing is proposed to* | detection from the perspective of multi-scale granular | Outlier detection refers to the identification | of anomalous samples that deviate significantly | from the distribution of normal data and has been

关键词： Anomaly detection | Rough sets | Information systems | Entropy | Accuracy | Support vector machines | Indexes | Weight measurement | Video surveillance | Urban areas

3. 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： disambiguation technique is devised based on the granular ball | disambiguation from the perspective of cognition computing. By | simultaneously incorporates cognition computing* from both* | Partial multi-label learning (PML) addresses | the issue of training a multi-label predictor in the

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

4. A Weighted Fuzzy Clustering Method Based on Granular-Ball Computing NSTL国家科技图书文献中心

Qiao Deng | Jiang Xie ... - 《2024 9th International Conference on Computer and Communication Systems,[v.1]》 - International Conference on Computer and Communication Systems - 2024, - 1350～1355 - 共6页

摘要： computing. Firstly, the relevant fuzzy granular-ball set | weighted fuzzy clustering method based on granular-ball | then the generated fuzzy granular-balls are connected | Existing weighted fuzzy clustering methods | typically use feature weighting techniques to eliminate

关键词： Accuracy | Communication systems | Clustering methods | Clustering algorithms | Robustness | Noise measurement

5. SGBCD: A Data Discretization Algorithm Based on Granular-ball Computing and Attribute Significance NSTL国家科技图书文献中心

Weiqi Chen - 《2024 IEEE 3rd International Conference on Electrical Engineering, Big Data and Algorithms: EEBDA 2024, Changchun, China, 27-29 February 2024, [v.1]》 - IEEE International Conference on Electrical Engineering, Big Data and Algorithms - 2024, - 47～51 - 共5页

摘要： discretization algorithm based on granular-ball* computing and* | introducing the granular-ball* computing, the difficulty of* | As an important method in data preprocessing | , discretization can effectively reduce the size of data | , generate concise semantic representation, obtain valuable

关键词： Electrical engineering | Machine learning algorithms | Semantics | Machine learning | Transforms | Big Data | Partitioning algorithms

6. Three-Way Approximations Fusion With Granular-Ball Computing to Guide Multigranularity Fuzzy Entropy for Feature Selection NSTL国家科技图书文献中心

Deyou Xia | Guoyin Wang ... - 《IEEE Transactions on Fuzzy Systems》 - 2024,32(10) - 5963～5977 - 共15页

摘要：. Driven by this, both granular-ball* (GB) computing and* | and TWA computing* are integrated to compress the* | In large-scale decision systems with high | dimensions, constructing an efficient feature selection | method via an uncertainty measure, has become a

关键词： Feature extraction | Uncertainty | Measurement uncertainty | Entropy | Costs | Rough sets | Fuzzy sets

7. Granular-ball computing-based manifold clustering algorithms for ultra-scalable data NSTL国家科技图书文献中心

Dongdong Cheng | Shushu Liu ... - 《Expert Systems with Application》 - 2024,247(Aug.) - 123313.1～123313.13 - 共13页

摘要： time-consuming. In this paper, we introduce Granular | -ball* (GB) into unsupervised manifold learning* | Manifold learning is essential for analyzing | high-dimensional data, but it suffers from high time | complexity. To address this, researchers proposed using

关键词： Granular-ball | Manifold learning | Ultra-scalable data | Spectral clustering | Ensemble clustering

8. GBTWSVM: Granular-Ball Twin Support Vector Machine NSTL国家科技图书文献中心

Lixi Zhao | Zhifei Zhang ... - 《Rough Sets: International Joint Conference, IJCRS 2024, Halifax, NS, Canada, May 17-20, 2024, Proceedings, Part I》 - International Joint Conference on Rough Sets - 2024, - 238～251 - 共14页

摘要： outliers. Granular-ball* computing (GBC), known for its* | Granular-ball Twin Support Vector Machine (GBTWSVM | Granular-ball Support Vector Machine (GBSVM) in terms of | optimize TWSVM by using coarse-grained granular-balls as | Twin Support Vector Machine (TWSVM) has gained

关键词： Granular-ball | Twin support vector machine | Granular-ball computing | Classification

9. Partial multi-label learning via robust feature selection and relevance fusion optimization NSTL国家科技图书文献中心

Qian W. | Tu Y.... - 《Knowledge-based systems》 - 2024,286(Feb.28) - 1.1～1.15 - 共15页 - 被引量：1

摘要： PML method jointly granular* ball-based robust* | a granular* ball to compute the core-set with* | ? 2024 Elsevier B.V.Partial Multi-Label | Learning (PML) is a more practical learning paradigm, in | which the labeling information is ambiguated. Most

关键词： Feature selection | Granular ball computing | Label confidence | Partial multi-label learning | Relevance optimization

10. An adaptive density clustering approach with multi-granularity fusion NSTL国家科技图书文献中心

Xie, Jiang | Jiang, Lian ... - 《Information Fusion》 - 2024,106 - 102273～ - 共15页

摘要： natural granular-ball. After processing these natural | granular-ball, overlapping ones are fused to yield the | The real -world dataset exhibits diversity | , incorporating instances with complex shapes and significant | differences in density hierarchy, potentially disrupted by

摘要： (GBRS) based on the granular-ball* computing combining* | deficit, this article presents a granular-ball* rough set* | -ball* computing. The GBRS can simultaneously represent* | the robustness and the adaptability of the granular | Pawlak rough set (PRS) and neighborhood rough

关键词： Rough sets | Feature extraction | Filtering theory | Computational modeling | Filtering algorithms | Classification algorithms | Training

摘要： based on granular-ball* computing is proposed to* | detection from the perspective of multi-scale granular | Outlier detection refers to the identification | of anomalous samples that deviate significantly | from the distribution of normal data and has been

关键词： Anomaly detection | Rough sets | Information systems | Entropy | Accuracy | Support vector machines | Indexes | Weight measurement | Video surveillance | Urban areas

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

摘要： computing. Firstly, the relevant fuzzy granular-ball set | weighted fuzzy clustering method based on granular-ball | then the generated fuzzy granular-balls are connected | Existing weighted fuzzy clustering methods | typically use feature weighting techniques to eliminate

关键词： Accuracy | Communication systems | Clustering methods | Clustering algorithms | Robustness | Noise measurement

关键词： Electrical engineering | Machine learning algorithms | Semantics | Machine learning | Transforms | Big Data | Partitioning algorithms

摘要：. Driven by this, both granular-ball* (GB) computing and* | and TWA computing* are integrated to compress the* | In large-scale decision systems with high | dimensions, constructing an efficient feature selection | method via an uncertainty measure, has become a

关键词： Feature extraction | Uncertainty | Measurement uncertainty | Entropy | Costs | Rough sets | Fuzzy sets

摘要： time-consuming. In this paper, we introduce Granular | -ball* (GB) into unsupervised manifold learning* | Manifold learning is essential for analyzing | high-dimensional data, but it suffers from high time | complexity. To address this, researchers proposed using

关键词： Granular-ball | Manifold learning | Ultra-scalable data | Spectral clustering | Ensemble clustering

摘要： outliers. Granular-ball* computing (GBC), known for its* | Granular-ball Twin Support Vector Machine (GBTWSVM | Granular-ball Support Vector Machine (GBSVM) in terms of | optimize TWSVM by using coarse-grained granular-balls as | Twin Support Vector Machine (TWSVM) has gained

关键词： Granular-ball | Twin support vector machine | Granular-ball computing | Classification

摘要： PML method jointly granular* ball-based robust* | a granular* ball to compute the core-set with* | ? 2024 Elsevier B.V.Partial Multi-Label | Learning (PML) is a more practical learning paradigm, in | which the labeling information is ambiguated. Most

关键词： Feature selection | Granular ball computing | Label confidence | Partial multi-label learning | Relevance optimization

摘要： natural granular-ball. After processing these natural | granular-ball, overlapping ones are fused to yield the | The real -world dataset exhibits diversity | , incorporating instances with complex shapes and significant | differences in density hierarchy, potentially disrupted by

关键词： Clustering | Natural neighbor | Multi-granularity cognitive learning | Granular-ball computing | NEIGHBOR | ALGORITHM | CORE

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： (GBRS) based on the granular-ball computing combining | deficit, this article presents a granular-ball rough set | -ball computing. The GBRS can simultaneously represent | the robustness and the adaptability of the granular | Pawlak rough set (PRS) and neighborhood rough

关键词： Rough sets | Feature extraction | Filtering theory | Computational modeling | Filtering algorithms | Classification algorithms | Training

摘要： based on granular-ball computing is proposed to | detection from the perspective of multi-scale granular | Outlier detection refers to the identification | of anomalous samples that deviate significantly | from the distribution of normal data and has been

关键词： Anomaly detection | Rough sets | Information systems | Entropy | Accuracy | Support vector machines | Indexes | Weight measurement | Video surveillance | Urban areas

摘要： disambiguation technique is devised based on the granular ball | disambiguation from the perspective of cognition computing. By | simultaneously incorporates cognition computing from both | Partial multi-label learning (PML) addresses | the issue of training a multi-label predictor in the

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

摘要： computing. Firstly, the relevant fuzzy granular-ball set | weighted fuzzy clustering method based on granular-ball | then the generated fuzzy granular-balls are connected | Existing weighted fuzzy clustering methods | typically use feature weighting techniques to eliminate

关键词： Accuracy | Communication systems | Clustering methods | Clustering algorithms | Robustness | Noise measurement

关键词： Electrical engineering | Machine learning algorithms | Semantics | Machine learning | Transforms | Big Data | Partitioning algorithms

摘要：. Driven by this, both granular-ball (GB) computing and | and TWA computing are integrated to compress the | In large-scale decision systems with high | dimensions, constructing an efficient feature selection | method via an uncertainty measure, has become a

关键词： Feature extraction | Uncertainty | Measurement uncertainty | Entropy | Costs | Rough sets | Fuzzy sets

摘要： time-consuming. In this paper, we introduce Granular | -ball (GB) into unsupervised manifold learning | Manifold learning is essential for analyzing | high-dimensional data, but it suffers from high time | complexity. To address this, researchers proposed using

关键词： Granular-ball | Manifold learning | Ultra-scalable data | Spectral clustering | Ensemble clustering

摘要： outliers. Granular-ball computing (GBC), known for its | Granular-ball Twin Support Vector Machine (GBTWSVM | Granular-ball Support Vector Machine (GBSVM) in terms of | optimize TWSVM by using coarse-grained granular-balls as | Twin Support Vector Machine (TWSVM) has gained

关键词： Granular-ball | Twin support vector machine | Granular-ball computing | Classification

摘要： PML method jointly granular ball-based robust | a granular ball to compute the core-set with | ? 2024 Elsevier B.V.Partial Multi-Label | Learning (PML) is a more practical learning paradigm, in | which the labeling information is ambiguated. Most

关键词： Feature selection | Granular ball computing | Label confidence | Partial multi-label learning | Relevance optimization

摘要： natural granular-ball. After processing these natural | granular-ball, overlapping ones are fused to yield the | The real -world dataset exhibits diversity | , incorporating instances with complex shapes and significant | differences in density hierarchy, potentially disrupted by

关键词： Clustering | Natural neighbor | Multi-granularity cognitive learning | Granular-ball computing | NEIGHBOR | ALGORITHM | CORE

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： (GBRS) based on the granular-ball* computing combining* | deficit, this article presents a granular-ball* rough set* | -ball* computing. The GBRS can simultaneously represent* | the robustness and the adaptability of the granular | Pawlak rough set (PRS) and neighborhood rough

摘要： based on granular-ball* computing is proposed to* | detection from the perspective of multi-scale granular | Outlier detection refers to the identification | of anomalous samples that deviate significantly | from the distribution of normal data and has been

摘要：. Driven by this, both granular-ball* (GB) computing and* | and TWA computing* are integrated to compress the* | In large-scale decision systems with high | dimensions, constructing an efficient feature selection | method via an uncertainty measure, has become a

摘要： time-consuming. In this paper, we introduce Granular | -ball* (GB) into unsupervised manifold learning* | Manifold learning is essential for analyzing | high-dimensional data, but it suffers from high time | complexity. To address this, researchers proposed using

摘要： outliers. Granular-ball* computing (GBC), known for its* | Granular-ball Twin Support Vector Machine (GBTWSVM | Granular-ball Support Vector Machine (GBSVM) in terms of | optimize TWSVM by using coarse-grained granular-balls as | Twin Support Vector Machine (TWSVM) has gained

摘要： PML method jointly granular* ball-based robust* | a granular* ball to compute the core-set with* | ? 2024 Elsevier B.V.Partial Multi-Label | Learning (PML) is a more practical learning paradigm, in | which the labeling information is ambiguated. Most