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Symmetry
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Recent Advances in Granular Computing for Intelligent Data Analysis
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Recent Advances in Granular Computing for Intelligent Data Analysis

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 22711

Special Issue Editors

Dr. Weihua Xu

E-Mail Website
Guest Editor
College of Artificial Intelligence, Southwest University, Chongqing 400715, China
Interests: fuzzy set; data mining; granular computing; information fusion; artifical intelligence; cognitive computing
Dr. Yanhong She

E-Mail Website
Guest Editor
School of Science, Xi’an Shiyou University, Xi’an 710065, China
Interests: granular computing; machine learning; uncertainty reasoning
Dr. Xiaoyan Zhang

E-Mail Website
Guest Editor
College of Artificial Intelligence, Southwest University, Chongqing 400715, China
Interests: mathematical basis of artificial intelligence; intelligent computing; granular computing; knowledge discovery; uncertainty processing

Special Issue Information

Dear Colleagues,

Granular computing constitutes an extensive body of knowledge that encompasses the unification of individual information granules (established within various settings, including set theory, interval calculus, fuzzy sets, rough sets, shadowed sets, and probabilistic granules) to form a coherent methodological and developmental environment. Granular computing represents a powerful tool for multiple granularity and multiple-view data analysis at different granularity levels, which has demonstrated strong capabilities and advantages in intelligent data analysis, pattern recognition, machine learning, and uncertain reasoning. In recent years, many excellent research results have been achieved in the field of multi-granularity computing involving models from two completely symmetrical positions, one optimistic and the other pessimistic.

Symmetry covers a broad spectrum of subjects in granular computing, embracing the theory, methodology, and application of the discipline in intelligent data analysis. Theoretical and applied studies involving fuzzy sets, interval analysis, rough sets, shadowed sets, and probabilistic sets, as well as related results in intelligent data analysis, are welcome.

This Special Issue focuses on the integration of both techniques through a granular computing approach to intelligent data analysis, especially regarding the design of efficient and effective integrated granular data analysis models, algorithms, and systems to improve reasoning and treatment of uncertain data.

Topics of interest for this issue include, but are not limited to:

  • Fuzzy set theory in data analysis;
  • Rough set theory;
  • Three-way decision theory;
  • Granular computing approach to machine learing;
  • Uncertainty reasoning;
  • Uncertainty analysis and granular computing;
  • Multigranularity data analysis;
  • Logical approach to data analysis;
  • Formal concept analysis;
  • Concept learning.

Dr. Weihua Xu
Dr. Yanhong She
Dr. Xiaoyan Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data mining
  • intelligent data analysis
  • granular computing
  • multigranularity computing
  • three-way decision
  • uncertainty analysis

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Published Papers (11 papers)

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Research

21 pages, 14676 KiB  
Article
Forward Greedy Searching to κ-Reduct Based on Granular Ball
by Minhui Song, Jianjun Chen, Jingjing Song, Taihua Xu and Yan Fan
Symmetry 2023, 15(5), 996; https://doi.org/10.3390/sym15050996 - 27 Apr 2023
Cited by 2 | Viewed by 1527
Abstract
As a key part of data preprocessing, namely attribute reduction, is effectively applied in the rough set field. The purpose of attribute reduction is to prevent too many attributes from affecting classifier operations and reduce the dimensionality of data space. Presently, in order [...] Read more.
As a key part of data preprocessing, namely attribute reduction, is effectively applied in the rough set field. The purpose of attribute reduction is to prevent too many attributes from affecting classifier operations and reduce the dimensionality of data space. Presently, in order to further improve the simplification performance of attribute reduction, numerous researchers have proposed a variety of methods. However, given the current findings, the challenges are: to reasonably compress the search space of candidate attributes; to fulfill multi-perspective evaluation; and to actualize attribute reduction based on guidance. In view of this, forward greedy searching to κ-reduct based on granular ball is proposed, which has the following advantages: (1) forming symmetrical granular balls to actualize the grouping of the universe; (2) continuously merging small universes to provide guidance for subsequent calculations; and (3) combining supervised and unsupervised perspectives to enrich the viewpoint of attribute evaluation and better improve the capability of attribute reduction. Finally, based on three classifiers, 16 UCI datasets are used to compare our proposed method with six advanced algorithms about attribute reduction and an algorithm without applying any attribute reduction algorithms. The experimental results indicate that our method can not only ensure the result of reduction has considerable performance in the classification test, but also improve the stability of attribute reduction to a certain degree. Full article
(This article belongs to the Special Issue Recent Advances in Granular Computing for Intelligent Data Analysis)
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17 pages, 5078 KiB  
Article
Dynamic Dual-Threshold Virtual Machine Merging Method Based on Three-Way Decision
by Jin Yang and Guoming Zhang
Symmetry 2022, 14(9), 1865; https://doi.org/10.3390/sym14091865 - 7 Sep 2022
Cited by 4 | Viewed by 1635
Abstract
Cloud computing, an emerging computing paradigm, has been widely considered due to its high scalability and availability. An essential stage of cloud computing is the cloud virtual machine migration technology. Nevertheless, the current trigger timing of virtual machine migration in cloud data centers [...] Read more.
Cloud computing, an emerging computing paradigm, has been widely considered due to its high scalability and availability. An essential stage of cloud computing is the cloud virtual machine migration technology. Nevertheless, the current trigger timing of virtual machine migration in cloud data centers is inaccurate, resulting in insufficient virtual machine consolidation. Furthermore, the high and low workload fluctuations are also a potential symmetrical problem worthy of attention. This paper proposes a virtual machine energy-saving merging method based on a three-way decision (ESMM-3WD). Firstly, we need to calculate the load fluctuation of the physical machine and divide the load fluctuation into three parts. Furthermore, the corresponding mathematical model predicts the load according to the different classification categories. Then, the predicted load value is used to dynamically adjust the threshold to improve the virtual machine merge probability. Finally, the simulation experiment is carried out on the cloud computing simulation platform cloudsim plus. The experimental results show that the virtual machine energy-saving merging method based on the three-way decision proposed in this paper can better reduce the number of migrations, increase the number of physical machines shut down, better improve the probability of virtual machine merger, and achieve the purpose of reducing the energy consumption of the data center. Full article
(This article belongs to the Special Issue Recent Advances in Granular Computing for Intelligent Data Analysis)
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18 pages, 2178 KiB  
Article
Attribute Network Representation Learning with Dual Autoencoders
by Jinghong Wang, Zhixia Zhou, Bi Li and Mancai Wu
Symmetry 2022, 14(9), 1840; https://doi.org/10.3390/sym14091840 - 5 Sep 2022
Cited by 1 | Viewed by 1549
Abstract
The purpose of attribute network representation learning is to learn the low-dimensional dense vector representation of nodes by combining structure and attribute information. The current network representation learning methods have insufficient interaction with structure when learning attribute information, and the structure and attribute [...] Read more.
The purpose of attribute network representation learning is to learn the low-dimensional dense vector representation of nodes by combining structure and attribute information. The current network representation learning methods have insufficient interaction with structure when learning attribute information, and the structure and attribute information cannot be well integrated. In this paper, we propose an attribute network representation learning method for dual-channel autoencoder. One channel is for the network structure, and adopting the multi-hop attention mechanism is used to capture the node’s high-order neighborhood information and calculate the neighborhood weight; The other channel is for the node attribute information, and a low-pass Laplace filter is designed to iteratively obtain the attribute information in the neighborhood of the node. The dual-channel autoencoder ensures the learning of structure and attribute information respectively. The adaptive fusion module is constructed in this method to increase the acquisition of important information through the consistency and difference constraints of two kinds of information. The method trains encoders by supervising the joint reconstruction of loss functions of two autoencoders. Based on the node clustering task on four authentic open data sets, and compared with eight network representation learning algorithms in clustering accuracy, standardized mutual information and running time of some algorithms, the experimental results show that the proposed method is superior and reasonable. Full article
(This article belongs to the Special Issue Recent Advances in Granular Computing for Intelligent Data Analysis)
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16 pages, 348 KiB  
Article
Attribute Reduction Based on Lift and Random Sampling
by Qing Chen, Taihua Xu and Jianjun Chen
Symmetry 2022, 14(9), 1828; https://doi.org/10.3390/sym14091828 - 3 Sep 2022
Cited by 7 | Viewed by 1515
Abstract
As one of the key topics in the development of neighborhood rough set, attribute reduction has attracted extensive attentions because of its practicability and interpretability for dimension reduction or feature selection. Although the random sampling strategy has been introduced in attribute reduction to [...] Read more.
As one of the key topics in the development of neighborhood rough set, attribute reduction has attracted extensive attentions because of its practicability and interpretability for dimension reduction or feature selection. Although the random sampling strategy has been introduced in attribute reduction to avoid overfitting, uncontrollable sampling may still affect the efficiency of search reduct. By utilizing inherent characteristics of each label, Multi-label learning with Label specIfic FeaTures (Lift) algorithm can improve the performance of mathematical modeling. Therefore, here, it is attempted to use Lift algorithm to guide the sampling for reduce the uncontrollability of sampling. In this paper, an attribute reduction algorithm based on Lift and random sampling called ARLRS is proposed, which aims to improve the efficiency of searching reduct. Firstly, Lift algorithm is used to choose the samples from the dataset as the members of the first group, then the reduct of the first group is calculated. Secondly, random sampling strategy is used to divide the rest of samples into groups which have symmetry structure. Finally, the reducts are calculated group-by-group, which is guided by the maintenance of the reducts’ classification performance. Comparing with other 5 attribute reduction strategies based on rough set theory over 17 University of California Irvine (UCI) datasets, experimental results show that: (1) ARLRS algorithm can significantly reduce the time consumption of searching reduct; (2) the reduct derived from ARLRS algorithm can provide satisfying performance in classification tasks. Full article
(This article belongs to the Special Issue Recent Advances in Granular Computing for Intelligent Data Analysis)
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17 pages, 675 KiB  
Article
An Improved Three-Way K-Means Algorithm by Optimizing Cluster Centers
by Qihang Guo, Zhenyu Yin and Pingxin Wang
Symmetry 2022, 14(9), 1821; https://doi.org/10.3390/sym14091821 - 2 Sep 2022
Cited by 5 | Viewed by 2014
Abstract
Most of data set can be represented in an asymmetric matrix. How to mine the uncertain information from the matrix is the primary task of data processing. As a typical unsupervised learning method, three-way k-means clustering algorithm uses core region and fringe region [...] Read more.
Most of data set can be represented in an asymmetric matrix. How to mine the uncertain information from the matrix is the primary task of data processing. As a typical unsupervised learning method, three-way k-means clustering algorithm uses core region and fringe region to represent clusters, which can effectively deal with the problem of inaccurate decision-making caused by inaccurate information or insufficient data. However, same with k-means algorithm, three-way k-means also has the problems that the clustering results are dependent on the random selection of clustering centers and easy to fall into the problem of local optimization. In order to solve this problem, this paper presents an improved three-way k-means algorithm by integrating ant colony algorithm and three-way k-means. Through using the random probability selection strategy and the positive and negative feedback mechanism of pheromone in ant colony algorithm, the sensitivity of the three k-means clustering algorithms to the initial clustering center is optimized through continuous updating iterations, so as to avoid the clustering results easily falling into local optimization. Dynamically adjust the weights of the core domain and the boundary domain to avoid the influence of artificially set parameters on the clustering results. The experiments on UCI data sets show that the proposed algorithm can improve the performances of three-way k-means clustering results and is effective in revealing cluster structures. Full article
(This article belongs to the Special Issue Recent Advances in Granular Computing for Intelligent Data Analysis)
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17 pages, 6142 KiB  
Article
Graph Mixed Random Network Based on PageRank
by Qianli Ma, Zheng Fan, Chenzhi Wang and Hongye Tan
Symmetry 2022, 14(8), 1678; https://doi.org/10.3390/sym14081678 - 12 Aug 2022
Cited by 4 | Viewed by 2079
Abstract
In recent years, graph neural network algorithm (GNN) for graph semi-supervised classification has made great progress. However, in the task of node classification, the neighborhood size is often difficult to expand. The propagation of nodes always only considers the nearest neighbor nodes. Some [...] Read more.
In recent years, graph neural network algorithm (GNN) for graph semi-supervised classification has made great progress. However, in the task of node classification, the neighborhood size is often difficult to expand. The propagation of nodes always only considers the nearest neighbor nodes. Some algorithms usually approximately classify by message passing between direct (single-hop) neighbors. This paper proposes a simple and effective method, named Graph Mixed Random Network Based on PageRank (PMRGNN) to solve the above problems. In PMRGNN, we design a PageRank-based random propagation strategy for data augmentation. Then, two feature extractors are used in combination to supplement the mutual information between features. Finally, a graph regularization term is designed, which can find more useful information for classification results from neighbor nodes to improve the performance of the model. Experimental results on graph benchmark datasets show that the method of this paper outperforms several recently proposed GNN baselines on the semi-supervised node classification. In the research of over-smoothing and generalization, PMRGNN always maintains better performance. In classification visualization, it is more intuitive than other classification methods. Full article
(This article belongs to the Special Issue Recent Advances in Granular Computing for Intelligent Data Analysis)
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12 pages, 536 KiB  
Article
Constructing Adaptive Multi-Scale Feature via Transformer-Aware Patch for Occluded Person Re-Identification
by Zhi Liu, Xingyu Mu, Shidu Dong, Yunhua Lu and Mingzi Jiang
Symmetry 2022, 14(7), 1454; https://doi.org/10.3390/sym14071454 - 15 Jul 2022
Viewed by 1759
Abstract
Person re-identification (Re-ID) aims to retrieve a specific pedestrian across a multi-disjoint camera in a surveillance system. Most of the research is based on a strong assumption that images should contain a full human torso. However, it cannot be guaranteed that all the [...] Read more.
Person re-identification (Re-ID) aims to retrieve a specific pedestrian across a multi-disjoint camera in a surveillance system. Most of the research is based on a strong assumption that images should contain a full human torso. However, it cannot be guaranteed that all the people have a clear foreground because they are out of constraint. In the real world, a variety of occluded situations frequently appear in video monitoring, which impedes the recognition process. To settle the occluded person Re-ID issue, a new Dual-Transformer symmetric architecture is proposed in this work, which can reduce the occluded impact and build a multi-scale feature. There are two contributions to our proposed model. (i) A Transformer-Aware Patch Searching (TAPS) module is devised to learn visible human region distribution using a multiheaded self-attention mechanism and construct a branch of distributed information attention scale. (ii) An Adaptive Visible-Part Cropping (AVPC) Strategy, with two steps of cropping and weakly-supervised learning, is used to generate a fine-scale visible image for another branch. Only ID labels are utilized to restrain TAPS and AVPC without any extra visible-part annotation. Extensive experiments are conducted on two occluded person Re-ID benchmarks, confirming that our approach performs a SOTA or comparable effect. Full article
(This article belongs to the Special Issue Recent Advances in Granular Computing for Intelligent Data Analysis)
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29 pages, 550 KiB  
Article
An Ensemble Framework to Forest Optimization Based Reduct Searching
by Jin Wang, Yuxin Liu, Jianjun Chen and Xibei Yang
Symmetry 2022, 14(6), 1277; https://doi.org/10.3390/sym14061277 - 20 Jun 2022
Cited by 3 | Viewed by 1703
Abstract
Essentially, the solution to an attribute reduction problem can be viewed as a reduct searching process. Currently, among various searching strategies, meta-heuristic searching has received extensive attention. As a new emerging meta-heuristic approach, the forest optimization algorithm (FOA) is introduced to the problem [...] Read more.
Essentially, the solution to an attribute reduction problem can be viewed as a reduct searching process. Currently, among various searching strategies, meta-heuristic searching has received extensive attention. As a new emerging meta-heuristic approach, the forest optimization algorithm (FOA) is introduced to the problem solving of attribute reduction in this study. To further improve the classification performance of selected attributes in reduct, an ensemble framework is also developed: firstly, multiple reducts are obtained by FOA and data perturbation, and the structure of those multiple reducts is symmetrical, which indicates that no order exists among those reducts; secondly, multiple reducts are used to execute voting classification over testing samples. Finally, comprehensive experiments on over 20 UCI datasets clearly validated the effectiveness of our framework: it is not only beneficial to output reducts with superior classification accuracies and classification stabilities but also suitable for data pre-processing with noise. This improvement work we have performed makes the FOA obtain better benefits in the data processing of life, health, medical and other fields. Full article
(This article belongs to the Special Issue Recent Advances in Granular Computing for Intelligent Data Analysis)
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16 pages, 996 KiB  
Article
TEXT Analysis on Ocean Engineering Equipment Industry Policies in China between 2010 and 2020
by Jiajia Ren and Shilun Ge
Symmetry 2022, 14(6), 1115; https://doi.org/10.3390/sym14061115 - 28 May 2022
Cited by 4 | Viewed by 2426
Abstract
The ocean engineering equipment industry is the foundation for the implementation of maritime strategy. China’s national departments at all levels have developed relevant ocean engineering equipment industry policies to promote the rapid development of the industry. By using 56 industrial policies issued between [...] Read more.
The ocean engineering equipment industry is the foundation for the implementation of maritime strategy. China’s national departments at all levels have developed relevant ocean engineering equipment industry policies to promote the rapid development of the industry. By using 56 industrial policies issued between 2010 and 2020 as the research sample, we conducted an in-depth assessment of the external structural characteristics and structure of the main cooperation network for such policies using descriptive statistics and social network analysis. Based on a symmetric analysis method, the two-dimensional matrix of cooperation breadth and cooperation depth, together with the measurement of the issuing subject’s centrality, was used to analyze the evolution of the subject’s role in the network. The research shows that the development of China’s ocean engineering equipment industry policies can be divided into three stages, and there are the following problems during the development of policies: (1) some policies and regulations are imperfect; (2) the network of cooperation among joint issuers is limited; and (3) some policies are issued by multiple government departments, but there is a lack of specialized and unified management from an absolute core department. Based on the above problems, we present some suggestions for policy optimization at the end of this paper. Full article
(This article belongs to the Special Issue Recent Advances in Granular Computing for Intelligent Data Analysis)
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22 pages, 3389 KiB  
Article
A Classification Model with Cognitive Reasoning Ability
by Jinghong Wang, Daipeng Zhang and Lina Liang
Symmetry 2022, 14(5), 1034; https://doi.org/10.3390/sym14051034 - 18 May 2022
Cited by 3 | Viewed by 2028
Abstract
In this paper, we study the classification problem of large data with many features and strong feature dependencies. This type of problem has shortcomings when handled by machine learning models. Therefore, a classification model with cognitive reasoning ability is proposed. The core idea [...] Read more.
In this paper, we study the classification problem of large data with many features and strong feature dependencies. This type of problem has shortcomings when handled by machine learning models. Therefore, a classification model with cognitive reasoning ability is proposed. The core idea is to use cognitive reasoning mechanism proposed in this paper to solve the classification problem of large structured data with multiple features and strong correlation between features, and then implements cognitive reasoning for features. The model has three parts. The first part proposes a Feature-to-Image algorithm for converting structured data into image data. The algorithm quantifies the dependencies between features, so as to take into account the impact of individual independent features and correlations between features on the prediction results. The second part designs and implements low-level feature extraction of the quantified features using convolutional neural networks. With the relative symmetry of the capsule network, the third part proposes a cognitive reasoning mechanism to implement high-level feature extraction, feature cognitive reasoning, and classification tasks of the data. At the same time, this paper provides the derivation process and algorithm description of cognitive reasoning mechanism. Experiments show that our model is efficient and outperforms comparable models on the category prediction experiment of ADMET properties of five compounds.This work will provide a new way for cognitive computing of intelligent data analysis. Full article
(This article belongs to the Special Issue Recent Advances in Granular Computing for Intelligent Data Analysis)
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16 pages, 2681 KiB  
Article
DII-GCN: Dropedge Based Deep Graph Convolutional Networks
by Jinde Zhu, Guojun Mao and Chunmao Jiang
Symmetry 2022, 14(4), 798; https://doi.org/10.3390/sym14040798 - 12 Apr 2022
Cited by 6 | Viewed by 3200
Abstract
Graph neural networks (GNNs) have gradually become an important research branch in graph learning since 2005, and the most active one is unquestionably graph convolutional neural networks (GCNs). Although convolutional neural networks have successfully learned for images, voices, and texts, over-smoothing remains a [...] Read more.
Graph neural networks (GNNs) have gradually become an important research branch in graph learning since 2005, and the most active one is unquestionably graph convolutional neural networks (GCNs). Although convolutional neural networks have successfully learned for images, voices, and texts, over-smoothing remains a significant obstacle for non-grid graphs. In particular, because of the over-smoothing problem, most existing GCNs are only effective below four layers. This work proposes a novel GCN named DII-GCN that originally integrates Dropedge, Initial residual, and Identity mapping methods into traditional GCNs for mitigating over-smoothing. In the first step of the DII-GCN, the Dropedge increases the diversity of learning sample data and slows down the network’s learning speed to improve learning accuracy and reduce over-fitting. The initial residual is embedded into the convolutional learning units under the identity mapping in the second step, which extends the learning path and thus weakens the over-smoothing issue in the learning process. The experimental results show that the proposed DII-GCN achieves the purpose of constructing deep GCNs and obtains better accuracy than existing shallow networks. DII-GCN model has the highest 84.6% accuracy at 128 layers of the Cora dataset, highest 72.5% accuracy at 32 layers of the Citeseer dataset, highest 79.7% accuracy at 32 layers of the Pubmed dataset. Full article
(This article belongs to the Special Issue Recent Advances in Granular Computing for Intelligent Data Analysis)
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