Article
Computer Science, Artificial Intelligence
Ziming Luo, Can Gao, Jie Zhou
Summary: This study proposes a rough sets-based tri-trade model for partially labeled data. A new discernibility matrix is first proposed to consider both labeled and unlabeled data, and a beam search-based algorithm is provided to generate multiple semi-supervised reducts. Then, a tri-trade model is developed using three diverse semi-supervised reducts, with a data editing technique embedded to generate reliable pseudo-labels for unlabeled data. Theoretical analysis and comparative experiments on UCI datasets demonstrate that the proposed model effectively utilize unlabeled data to improve generalization performance and outperform other representative methods.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Pandu Sowkuntla, P. S. V. S. Sai Prasad
Summary: Fuzzy-rough set theory is an efficient method for attribute reduction, but current approaches are inefficient for large data sets. This paper introduces a fuzzy discernibility matrix-based attribute reduction accelerator that performs better than existing methods in terms of computational efficiency.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Zhong Yuan, Hongmei Chen, Peng Xie, Pengfei Zhang, Jia Liu, Tianrui Li
Summary: This paper investigated attribute reduction methods in fuzzy rough set theory, comparing and analyzing three different types of reduction rules through experiments, which can retain fewer attributes while improving or maintaining the classification accuracy of a classifier. Furthermore, some new research directions were discussed.
APPLIED SOFT COMPUTING
(2021)
Article
Mathematics, Applied
Imran Javaid, Shahroz Ali, Shahid Ur Rehman, Aqsa Shah
Summary: This paper investigates the theory of rough set in the context of graphs, using the concept of orbits. The authors introduce the indiscernibility relation based on orbits and prove conditions under which the partitions remain the same. They also study the rough membership functions for various types of graphs and introduce essential sets and discernibility matrices induced by orbits.
Article
Automation & Control Systems
Zhehuang Huang, Jinjin Li
Summary: The article introduces fuzzy beta covering to evaluate the uncertainty of datasets and proposes a discernibility measure to describe the distinguishing ability of fuzzy covering families. Various variant measures are presented to reflect changes in distinguishing ability caused by different fuzzy covering families. Finally, an algorithm is designed to reduce redundant fuzzy coverings and achieve knowledge reduction.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Jianhua Dai, Weisi Wang, Chucai Zhang, Shaojun Qu
Summary: This paper investigates the problem of semi-supervised attribute reduction in the context of rough set theory. Firstly, a semi-supervised attribute reduction algorithm is proposed by combining supervised and unsupervised discernibility pair strategies. Secondly, new methods for defining the similarity and distinction between attributes using discernibility pairs are introduced. Thirdly, a semi-supervised attribute reduction algorithm using indiscernible attribute classes is proposed.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Meng Hu, Eric C. C. Tsang, Yanting Guo, Degang Chen, Weihua Xu
Summary: This study introduces a novel attribute reduction method based on weighted neighborhood relations, which fully mines the correlation between attributes and decisions, assigning higher weights to attributes with higher correlation, achieving good performance results.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Information Systems
Guoqiang Wang, Tianrui Li, Pengfei Zhang, Qianqian Huang, Hongmei Chen
Summary: Local rough set models are effective for handling large data sets with small amounts of labeled data, improving computational performance significantly. The double-local rough set framework introduces the concept of local equivalence classes and defines lower deletion matrix, upper addition matrix, and upper deletion matrix. Proposed algorithms in double-local rough sets outperform original counterparts in attribute reduction and knowledge discovery.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Meng Hu, Yanting Guo, Degang Chen, Eric C. C. Tsang, Qingshuo Zhang
Summary: The construction of fuzzy relations is crucial in fuzzy rough sets, and relations generated by soft distances are more robust. Two enhanced fuzzy similarity relations are proposed to improve attribute reduction and classification, using neighborhood and decision information. The algorithm is validated using gene expression profiles and demonstrates strong noise resistance and selection of tumor-related genes.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yizhu Li, Mingjie Cai, Jie Zhou, Qingguo Li
Summary: The notion of multi-granularity has been applied in mathematical models in granular computing. This paper proposes an accelerated algorithm for multi-granularity reduction, addressing the challenges of high computational complexity and difficulty in synthesizing multi-granularity information. The method constructs a multi-granularity reduction structure and integrates multiple granularity information in attribute evaluations, achieving high-quality reduction results with improved time efficiency.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Information Systems
Zhong Yuan, Hongmei Chen, Tianrui Li, Zeng Yu, Binbin Sang, Chuan Luo
Summary: The study proposed a generalized unsupervised mixed attribute reduction model based on fuzzy rough sets and designed a specific algorithm FRUAR. Experimental results showed that the FRUAR algorithm can select fewer attributes to maintain or improve the performance of learning algorithms.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Chengling Zhang, Jinjin Li, Yidong Lin
Summary: This work introduces a novel knowledge reduction approach in incomplete information systems based on discernibility techniques in multigranulation rough set theory. By defining knowledge reduction concepts and decision functions, the study proposes a pessimistic lower (upper) approximation granular quality function.
Article
Computer Science, Information Systems
Meng Hu, Eric C. C. Tsang, Yanting Guo, Degang Chen, Weihua Xu
Summary: This paper proposes an attribute optimization algorithm based on overlap degree to accelerate attribute reduction and improve classification performance. By modeling the overlap degree of objects from different categories in advance, it can efficiently filter and optimize attributes for decision approximation.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Shuyin Xia, Shulin Wu, Xinxing Chen, Guoyin Wang, Xinbo Gao, Qinghua Zhang, Elisabeth Giem, Zizhong Chen
Summary: Feature selection is crucial in data mining and pattern recognition. We propose a novel neighborhood rough set model that does not rely on distance measures, resulting in improved accuracy and efficiency compared to traditional NRS algorithms. Experimental results demonstrate that our method outperforms existing NRS algorithms in terms of both accuracy and efficiency.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Theory & Methods
Linlin Xie, Guoping Lin, Jinjin Li, Yidong Lin
Summary: Attribute reduction, an essential challenge in pattern recognition, data mining, and knowledge discovery, can be improved by using a new measure called local information entropy.
FUZZY SETS AND SYSTEMS
(2023)