Article
Computer Science, Artificial Intelligence
Zhehuang Huang, Jinjin Li
Summary: This paper proposes a new data analysis model using multi-scale coverings for knowledge representation, and discusses optimal scale selection for consistent and inconsistent covering decision tables to obtain acceptable decisions. Experimental results show that the multi-scale covering theory can enhance the generalization ability of the classification model.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Theory & Methods
Marko Palangetic, Chris Cornelis, Salvatore Greco, Roman Slowinski
Summary: This paper discusses the importance of granular representations of crisp and fuzzy sets in rule induction algorithms based on rough set theory. It demonstrates that the OWA-based fuzzy rough set model, which has been successfully applied in various machine learning tasks, allows for a granular representation. The practical implications of this result for rule induction from fuzzy rough approximations are highlighted.
FUZZY SETS AND SYSTEMS
(2022)
Article
Computer Science, Theory & Methods
Yumin Chen, Shunzhi Zhu, Wei Li, Nan Qin
Summary: The study proposes a fuzzy granular convolutional classifier, which extracts features and optimizes weights through fuzzy granulation and convolutional operations, ultimately achieving better classification performance.
FUZZY SETS AND SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Xuan Yang, Bing Huang
Summary: In this study, we propose a dominance-based fuzzy rough set model for multi-scale decision tables with fuzzy condition attribute values and fuzzy decision attribute values. We improve the knowledge acquisition efficiency by introducing optimal scale selection and reduction methods and effectively integrating them with attribute reduction.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Pengfei Zhang, Tianrui Li, Chuan Luo, Guoqiang Wang
Summary: The paper introduces an Adaptive Multi-Granulation Decision-Theoretic Rough Sets (AMG-DTRS) model, which adaptively obtains probabilistic thresholds by setting a compensation coefficient. Three types of mean AMG-DTRS models are studied, offering a new perspective on information fusion. The advantages and generalization of the AMG-DTRS model are demonstrated by analyzing its connections and differences with existing MGRS models.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2022)
Article
Computer Science, Artificial Intelligence
Wei-Zhi Wu, Dongran Niu, Jinhai Li, Tong -Jun Li
Summary: This paper investigates knowledge acquisition in multi-scale information systems by deriving IF-THEN rules for multi-scale decision attributes. It introduces the concept of a generalized multi-scale decision information table (GMDIT) and defines scale selections for individual decision tables in GMDITs. The paper also describes information granules and their properties with different scale selections, formulates optimal scale selections for inconsistent GMDITs, and presents local optimal scale selections for obtaining concise decision rules. Finally, attribute reducts based on optimal scale selections are derived, and hidden decision rules in inconsistent GMDITs are unraveled.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2023)
Article
Computer Science, Information Systems
Yu Wang, Qinghua Hu, Hao Chen, Yuhua Qian
Summary: Hierarchical classification is a method for identifying samples by traversing from the root node to a leaf node along the hierarchical structures of labels. However, predicting leaf nodes is difficult due to ambiguous or incomplete information. This study proposes a multi-granularity decision method that takes into account the uncertainties in classifier predictions and intrinsic information of the data to ensure proper multi-granularity decisions.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Theory & Methods
Chun Yong Wang, Lijuan Wan
Summary: The study focuses on granular variable precision fuzzy rough sets based on fuzzy (co)implications to rectify defects, discussing equivalent expressions and composition. Further research looks into rectifying faults using appropriate semicontinuity.
FUZZY SETS AND SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Fenggang Han, Xiao Zhang, Linjie He, Liru Kong, Yumin Chen
Summary: In this paper, a framework of multimodal classification based on kernel functions and fuzzy granular computing is proposed. This framework can handle multiple types of multimodal features and address the challenges of complex structures and uncertain semantics.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Wenbin Qian, Fankang Xu, Jin Qian, Wenhao Shu, Weiping Ding
Summary: This paper proposes a rough granular-ball computing model for multi-label data clustering and feature selection. By clustering multi-label data into multiple granules that reflect the local information of instances, and converting logical labels into label distribution through a label enhancement approach. Additionally, a rough granular-ball-based feature selection method is proposed for enhanced label distribution data.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Jia-Wen Zheng, Wei-Zhi Wu, Han Bao, An-Hui Tan
Summary: This paper investigates the optimal scale selection for multi-scale ordered decision systems based on evidence theory and clarifies relationships among different types of optimal scales.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Jinbo Wang, Wei-Zhi Wu, Anhui Tan
Summary: This study investigates knowledge discovery in incomplete generalized multi-scale decision systems based on multi-granulation rough sets. It discusses the multi-granulation structures, defines pessimistic and optimistic optimal scale combinations, and explores reducts of scale combinations. Numerical algorithms are designed for finding optimal scale combinations.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Runkang Li, Jilin Yang, Xianyong Zhang
Summary: In this paper, we propose a method to transform linguistic set-values into numerical values based on granular structures for multi-scale decision tables. We construct a relatively objective and comprehensive total cost for optimal scale selection, including test cost, delay cost, and misclassification cost. An OSS algorithm is designed based on the order of uncertainty and total cost. The feasibility and effectiveness of the algorithm are verified through experiments on UCI data sets.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Zhanao Xue, Bingxin Sun, Haodong Hou, Wenli Pang, Yanna Zhang
Summary: This article proposes intuitionistic hesitant fuzzy sets and multi-granulation rough intuitionistic hesitant fuzzy set models, and establishes three-way decision models. The research results show that these models can effectively evaluate objects with different attitudes and provide decision-making solutions.
COGNITIVE COMPUTATION
(2022)
Article
Computer Science, Information Systems
Deyou Xia, Guoyin Wang, Jie Yang, Qinghua Zhang, Shuai Li
Summary: Local rough sets (LRS) is an effective model for processing large-scale datasets, and it measures uncertainty by establishing local rough approximation measure (LRAM) and local knowledge distance (LKD) and correlating the two as a feature selection algorithm. Experimental results demonstrate the feasibility of this uncertainty measure.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Wei-Zhi Wu, Ming-Wen Shao, Xia Wang
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2019)
Article
Computer Science, Artificial Intelligence
Anhui Tan, Wei-Zhi Wu, Yuhua Qian, Jiye Liang, Jinkun Chen, Jinjin Li
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2019)
Article
Computer Science, Information Systems
Bing Huang, Wei-Zhi Wu, Jinjiang Yan, Huaxiong Li, Xianzhong Zhou
INFORMATION SCIENCES
(2020)
Article
Computer Science, Interdisciplinary Applications
Tianjun Wu, Wen Dong, Jiancheng Luo, Yingwei Sun, Qiting Huang, Weizhi Wu, Xiaodong Hu
EARTH SCIENCE INFORMATICS
(2019)
Article
Computer Science, Artificial Intelligence
Anhui Tan, Wei-Zhi Wu, Suwei Shia, Shimei Zhao
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2019)
Article
Computer Science, Interdisciplinary Applications
Kai Zhang, Jianming Zhan, Weizhi Wu, Jose Carlos R. Alcantud
COMPUTERS & INDUSTRIAL ENGINEERING
(2019)
Article
Computer Science, Information Systems
Dechao Li, Yee Leung, Weizhi Wu
INFORMATION SCIENCES
(2019)
Article
Computer Science, Artificial Intelligence
Ming-Wen Shao, Wei-Zhi Wu, Chang-Zhong Wang
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2019)
Article
Computer Science, Artificial Intelligence
Han Bao, Wei-Zhi Wu, Jia-Wen Zheng, Tong-Jun Li
Summary: This paper explores the concept of entropy and its application in selecting optimal scale combinations in hierarchical data sets. It examines the relationship between entropy optimal scale combinations and classical optimal scale combinations, showing their equivalence in certain scenarios. The study ultimately verifies the effectiveness of entropy in maintaining uncertain measures of knowledge in multi-scale information tables.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2021)
Article
Computer Science, Artificial Intelligence
Kai Zhang, Jianming Zhan, Wei-Zhi Wu
Summary: This article introduces a novel fuzzy alpha-neighborhood operator and a fuzzy rough set model based on this operator for decision-making in information systems. By utilizing data normalization and the fuzzy alpha-neighborhood-based fuzzy rough set model, real-valued information systems are effectively transformed into intuitionistic fuzzy-valued information systems, with three different sorting decision-making schemes developed on the latter. The method is validated through numerical experiments and comparative studies, demonstrating its stability and effectiveness.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Automation & Control Systems
Anhui Tan, Suwei Shi, Wei-Zhi Wu, Jinjin Li, Witold Pedrycz
Summary: This article examines the application of granular structures of intuitionistic fuzzy information in data mining and information processing. It defines partial-order relations at different hierarchical levels to reveal the granularity of the structures, characterizes the granularity invariance between different structures using relational mappings, and generalizes Shannon's entropies to IF entropies. The significance of intuitionistic attributes using the information measures is introduced, and an information-preserving algorithm for data reduction of IF information systems is constructed. Numerical experiments confirm the performance of the proposed technique by inducing substantial IF relations from public datasets considering the similarity/diversity between samples from the same/different classes.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Jia-Wen Zheng, Wei-Zhi Wu, Han Bao, An-Hui Tan
Summary: This paper investigates the optimal scale selection for multi-scale ordered decision systems based on evidence theory and clarifies relationships among different types of optimal scales.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Jinbo Wang, Wei-Zhi Wu, Anhui Tan
Summary: This study investigates knowledge discovery in incomplete generalized multi-scale decision systems based on multi-granulation rough sets. It discusses the multi-granulation structures, defines pessimistic and optimistic optimal scale combinations, and explores reducts of scale combinations. Numerical algorithms are designed for finding optimal scale combinations.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Yu Sun, Wei-Zhi Wu, Xia Wang
Summary: This paper investigates the problem of selecting the optimal scale in numerical incomplete multi-scale information systems (NIMIS) and numerical incomplete multi-scale decision systems (NIMDS). By employing the maximal consistent block technique, the authors define the scale in NIMIS and NIMDS and propose the maximal consistent block based optimal scale. The results show that the maximal consistent block based optimal scale and the optimal scale are equivalent in consistent NIMDS, while there is no static relationship between the maximal consistent block based lower-approximation optimal scale and the upper-approximation optimal scale in inconsistent NIMDS.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Wei-Zhi Wu, Dongran Niu, Jinhai Li, Tong -Jun Li
Summary: This paper investigates knowledge acquisition in multi-scale information systems by deriving IF-THEN rules for multi-scale decision attributes. It introduces the concept of a generalized multi-scale decision information table (GMDIT) and defines scale selections for individual decision tables in GMDITs. The paper also describes information granules and their properties with different scale selections, formulates optimal scale selections for inconsistent GMDITs, and presents local optimal scale selections for obtaining concise decision rules. Finally, attribute reducts based on optimal scale selections are derived, and hidden decision rules in inconsistent GMDITs are unraveled.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2023)
