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Article
Computer Science, Artificial Intelligence
Weihua Xu et al.
Summary: This paper introduces a feature selection method based on neighborhood rough set and proposes a local composite entropy for dealing with imbalanced data in fuzzy data. Experimental results demonstrate that the proposed method achieves better classification performance in fuzzy data.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Weihua Xu et al.
Summary: This article proposes a novel fuzzy-based two-way concept cognitive learning method (TCCL) to address the issues and challenges in traditional learning methods and to perform concept learning in a dynamic environment.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Information Systems
Weihua Xu et al.
Summary: Information fusion is the process of integrating and transforming information from multiple sources into a unified representation. Existing methods are not suitable for multi-source fusion of interval-valued ordered data, and there is a need for efficient data fusion. This study proposes a fusion approach for multi-source interval-valued ordered data, introducing concepts such as fuzzy dominance relation and dominance classes. Incremental mechanisms and algorithms are also designed for updating multi-source interval-valued data. Experimental results demonstrate the effectiveness and efficiency of the proposed fusion approach.
IEEE TRANSACTIONS ON BIG DATA
(2023)
Article
Computer Science, Artificial Intelligence
Wentao Li et al.
Summary: This article proposes a method for dealing with interval ordered data using interval-valued dominance relation. It first introduces new thresholds for interval dominance degree and interval overlap degree, and then constructs the interval-valued dominance relation. Based on this relation, the interval-valued dominance-based rough set approach and its properties are investigated, and feature selection rules and algorithms are provided.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Tian Yang et al.
Summary: This paper proposes a local feature selection method based on related family, which can accelerate data processing for large-scale data sets. The experiments demonstrate that the proposed algorithm is 405 times faster than LARD on partially labeled data sets while maintaining high classification accuracy. Additionally, this algorithm can effectively process partially labeled large-scale data sets with 5,000,000 samples or 20,000 features on a typical personal computer.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Shuyin Xia et al.
Summary: Feature reduction is crucial in Big Data analytics, and rough sets are commonly used for attribute reduction. However, existing rough set algorithms have limitations in terms of efficiency and effectiveness. To address these issues, a novel method called granular ball neighborhood rough sets (GBNRS) is proposed, which outperforms the current state-of-the-art method in terms of performance and classification accuracy.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Information Systems
Jingqian Wang et al.
Summary: This paper proposes two new matrix methods to calculate minimal description (MinD) and maximal description (MaxD) in covering-based rough sets (CbRSs). The first method combines grained matrices and the mean of sum to calculate MinD and MaxD, while the second method introduces three new matrix operations and the notion of complementary matrix to simplify the calculation process. The computational efficiency of these approaches is demonstrated and compared with other methods using experiments on UCI data sets.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Zhehuang Huang et al.
Summary: This paper proposes a new robust rough set model by combining various rough set methods to address the limitations of the traditional fuzzy beta covering method in real data. By reconstructing the upper and lower approximations of the target concept, introducing a fuzzy dependence function to evaluate the classification ability, and utilizing a feature selection algorithm for dimensionality reduction, the proposed model demonstrates good robustness on datasets contaminated with noise and outperforms some state-of-the-art feature learning algorithms in terms of classification accuracy and the size of the selected feature subset.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Nguyen Ngoc Thuy et al.
Summary: This paper introduces an attribute reduction algorithm based on a stripped neighborhood cover, which uses an efficient heuristic algorithm to find an optimal feature subset in mixed and incomplete decision tables to reduce unnecessary tolerance classes. Experimental results show that the algorithm outperforms existing algorithms in classification accuracy.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Information Systems
Guilong Liu et al.
Summary: Attribute reduction is an important component in rough set theory and formal concept analysis, and the three-way concept lattice is a combination of concept lattices and three-way decision theory. This paper investigates granular reduction and distribution reduction in general formal decision contexts, and proposes reduction algorithms based on discernibility matrix for each type of reduction. The effectiveness of the three-way granular reduction algorithm is evaluated using 17 UCI datasets.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Fang Fang Zhao et al.
Summary: In this paper, a new approach to Generalized Neighborhood System-based Rough Sets (GNSs) is introduced from the perspectives of convex structures and convex matroids. The concepts of matroidal exterior operators and matroidal interior operators are proposed, and their relationships with convex matroids are investigated. Two new types of GNSs, co-directed intersection closed and exchangeable ones, are introduced, and their compatible relationships with convex structures and convex matroids are established. Axiomatic characterizations of the rough approximation operators for a co-directed intersection closed GNS operator and its combinations with other GNS operators are presented.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Jingliu Lai et al.
Summary: In this study, a novel semi-supervised feature selection model ASLCGLFS is proposed, which combines label information to extend adaptive graph learning for further improving the quality of the similarity matrix. Additionally, adaptive structure learning is introduced to consider global structure and facilitate feature selection.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Pengfei Zhang et al.
Summary: Feature selection is important for enhancing learning performance and readability by removing irrelevant or redundant features. The neighborhood rough set (NRS) is an effective method for handling heterogeneous data and has been successfully applied to feature selection.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Zhehuang Huang et al.
Summary: Fuzzy beta covering has been a topic of significant interest, but there are issues with reflexivity and noise handling. This study addresses these problems by introducing a reflexive fuzzy covering neighborhood and a variable precision discrimination index, and by designing a feature selection algorithm. The proposed methods are validated through experiments.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Giorgio Roffo et al.
Summary: The study introduces a filtering feature selection framework that considers feature subsets as paths in a graph to address relevance and redundancy principles. Two different interpretations are used to evaluate the values of feature subsets, and a simple unsupervised strategy is proposed to cut the ranking and provide the subset of features to keep. The experiments show that the Infinite Feature Selection framework performs better in almost any situation compared to widely-known comparative approaches.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)
Article
Computer Science, Information Systems
Jihong Wan et al.
Summary: Feature selection is a crucial data preprocessing approach in data mining, and the interaction between features and their dynamic changes should be taken into consideration to prevent the loss of useful information.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Theory & Methods
Jinkun Chen et al.
FUZZY SETS AND SYSTEMS
(2020)
Article
Computer Science, Artificial Intelligence
Guilong Liu et al.
KNOWLEDGE-BASED SYSTEMS
(2020)
Article
Computer Science, Artificial Intelligence
Mingjie Cai et al.
KNOWLEDGE-BASED SYSTEMS
(2019)
Article
Computer Science, Artificial Intelligence
Jianhua Dai et al.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2018)
Article
Computer Science, Artificial Intelligence
Guangming Lang et al.
KNOWLEDGE-BASED SYSTEMS
(2018)
Article
Computer Science, Information Systems
Qian Zhou et al.
Review
Automation & Control Systems
Witold Pedrycz
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2018)
Article
Computer Science, Artificial Intelligence
Changzhong Wang et al.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2017)
Article
Computer Science, Artificial Intelligence
Jinkun Chen et al.
KNOWLEDGE-BASED SYSTEMS
(2017)
Article
Computer Science, Information Systems
Jinkun Chen et al.
INFORMATION SCIENCES
(2015)
Article
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Anhui Tan et al.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2015)
Article
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Shiping Wang et al.
INFORMATION SCIENCES
(2014)
Article
Computer Science, Information Systems
Tian Yang et al.
INFORMATION SCIENCES
(2013)
Article
Computer Science, Artificial Intelligence
Tian Yang et al.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2010)
Article
Computer Science, Artificial Intelligence
Richard Jensen et al.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2009)
Article
Computer Science, Information Systems
William Zhu
INFORMATION SCIENCES
(2009)
Article
Computer Science, Information Systems
Changzhong Wang et al.
INFORMATION SCIENCES
(2008)
Article
Computer Science, Information Systems
Qinghua Hu et al.
INFORMATION SCIENCES
(2008)
Article
Computer Science, Information Systems
William Zhu
INFORMATION SCIENCES
(2007)
Article
Computer Science, Information Systems
Chen Degang et al.
INFORMATION SCIENCES
(2007)
Article
Computer Science, Artificial Intelligence
William Zhu et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2007)
