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Yin Y. Prediction and analysis of time series data based on granular computing. Front Comput Neurosci 2023; 17:1192876. [PMID: 37576071 PMCID: PMC10413556 DOI: 10.3389/fncom.2023.1192876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 07/06/2023] [Indexed: 08/15/2023] Open
Abstract
The advent of the Big Data era and the rapid development of the Internet of Things have led to a dramatic increase in the amount of data from various time series. How to classify, correlation rule mining and prediction of these large-sample time series data has a crucial role. However, due to the characteristics of high dimensionality, large data volume and transmission lag of sensor data, large sample time series data are affected by multiple factors and have complex characteristics such as multi-scale, non-linearity and burstiness. Traditional time series prediction methods are no longer applicable to the study of large sample time series data. Granular computing has unique advantages in dealing with continuous and complex data, and can compensate for the limitations of traditional support vector machines in dealing with large sample data. Therefore, this paper proposes to combine granular computing theory with support vector machines to achieve large-sample time series data prediction. Firstly, the definition of time series is analyzed, and the basic principles of traditional time series forecasting methods and granular computing are investigated. Secondly, in terms of predicting the trend of data changes, it is proposed to apply the fuzzy granulation algorithm to first convert the sample data into coarser granules. Then, it is combined with a support vector machine to predict the range of change of continuous time series data over a period of time. The results of the simulation experiments show that the proposed model is able to make accurate predictions of the range of data changes in future time periods. Compared with other prediction models, the proposed model reduces the complexity of the samples and improves the prediction accuracy.
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Affiliation(s)
- Yushan Yin
- School of Electro-Mechanical Engineering, Xidian University, Xi’an, China
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2
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Cui H, Deng A, Yue G, Zou L, Martinez L. The Linguistic Concept’s Reduction Methods under Symmetric Linguistic-Evaluation Information. Symmetry (Basel) 2023. [DOI: 10.3390/sym15040813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023] Open
Abstract
Knowledge reduction is a crucial topic in formal concept analysis. There always exists uncertain, symmetric linguistic-evaluation information in social life, which leads to high complexity in the process of knowledge representation. In order to overcome this problem, we are focused on studying the linguistic-concept-reduction methods in an uncertain environment with fuzzy linguistic information. Based on three-way decisions and an attribute-oriented concept lattice, we construct a fuzzy-object-induced three-way attribute-oriented linguistic (FOEAL) concept lattice, which provides complementary conceptual structures of a three-way concept lattice with symmetric linguistic-evaluation information. Through the granular concept of the FOEAL lattice, we present the corresponding linguistic concept granular consistent set and granular reduction. Then, we further employ the linguistic concept discernibility matrix and discernibility function to calculate the granular reduction set. A similar issue on information entropy is investigated to introduce a method of entropy reduction for the FOEAL lattice, and the relation between the linguistic concept granular reduction and entropy reduction is discussed. The efficiency of the proposed method is depicted by some examples and comparative analysis.
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Chen Y, Zhu P, Li Q, Yao Y. Granularity-driven trisecting-and-learning models for interval-valued rule induction. APPL INTELL 2023. [DOI: 10.1007/s10489-023-04468-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
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4
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Fan M, Luo S, Li J. Network rule extraction under the network formal context based on three-way decision. APPL INTELL 2023; 53:5126-5145. [PMID: 35756086 PMCID: PMC9205655 DOI: 10.1007/s10489-022-03672-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/21/2022] [Indexed: 01/17/2023]
Abstract
Knowledge discovery combined with network structure is an emerging field of network data analysis and mining. Three-way concept analysis is a method that can fit the human mind in uncertain decisions and analysis. In reality, when three-way concept analysis is placed in the background of a network, not only the three-way rules need to be obtained, but also the network characteristic values of these rules should be obtained, which is of great significance for concept cognition in the network. This paper mainly combines complex network analysis with the formal context of three-way decision. Firstly, the network formal context of three-way decision (NFC3WD) is proposed to unify the two studies mentioned above into one data framework. Then, the network weaken-concepts of three-way decision (NWC3WD) and their corresponding sub-networks are studied. Therefore, we can not only find out the network weaken-concepts but also know the average influence of the sub-network, as well as the influence difference within the sub-network. Furthermore, the concept logic of network and the properties of its operators are put forward, which lays a foundation for designing the algorithm of rule extraction. Subsequently, the bidirectional rule extraction algorithm and reduction algorithm based on confidence degree are also explored. Meanwhile, these algorithms are applied to the diagnosis examples of COVID-19 from which we can not only get diagnostic rules, but also know the importance of the population corresponding to these diagnostic rules in the network through network eigenvalues. Finally, experimental analysis is made to show the superiority of the proposed method.
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Affiliation(s)
- Min Fan
- grid.218292.20000 0000 8571 108XFaculty of Science, Kunming University of Science and Technology, Kunming, 650500 Yunnan People’s Republic of China ,grid.218292.20000 0000 8571 108XData Science Research Center, Kunming University of Science and Technology, Kunming, 650500 Yunnan People’s Republic of China
| | - Shan Luo
- grid.218292.20000 0000 8571 108XFaculty of Science, Kunming University of Science and Technology, Kunming, 650500 Yunnan People’s Republic of China ,grid.218292.20000 0000 8571 108XData Science Research Center, Kunming University of Science and Technology, Kunming, 650500 Yunnan People’s Republic of China
| | - Jinhai Li
- grid.218292.20000 0000 8571 108XFaculty of Science, Kunming University of Science and Technology, Kunming, 650500 Yunnan People’s Republic of China ,grid.218292.20000 0000 8571 108XData Science Research Center, Kunming University of Science and Technology, Kunming, 650500 Yunnan People’s Republic of China
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Wang Z, Miao D. Spatial-temporal single object tracking with three-way decision theory. Int J Approx Reason 2022. [DOI: 10.1016/j.ijar.2022.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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6
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Multi-granularity sequential three-way recommendation based on collaborative deep learning. Int J Approx Reason 2022. [DOI: 10.1016/j.ijar.2022.11.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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7
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Wang Z, Qi J, Shi C, Ren R, Wei L. Multiview granular data analytics based on three-way concept analysis. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04145-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Chen Y, Zhang X, Zhuang Y, Yao B, Lin B. Granular neural networks with a reference frame. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.110147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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9
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Ye P, Chen Y, Zhu F, Lv Y, Lu W, Wang FY. Bridging the Micro and Macro: Calibration of Agent-Based Model Using Mean-Field Dynamics. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:11397-11406. [PMID: 34232903 DOI: 10.1109/tcyb.2021.3089712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Calibration of agent-based models (ABM) is an essential stage when they are applied to reproduce the actual behaviors of distributed systems. Unlike traditional methods that suffer from the repeated trial and error and slow convergence of iteration, this article proposes a new ABM calibration approach by establishing a link between agent microbehavioral parameters and systemic macro-observations. With the assumption that the agent behavior can be formulated as a high-order Markovian process, the new approach starts with a search for an optimal transfer probability through a macrostate transfer equation. Then, each agent's microparameter values are computed using mean-field approximation, where his complex dependencies with others are approximated by an expected aggregate state. To compress the agent state space, principal component analysis is also introduced to avoid high dimensions of the macrostate transfer equation. The proposed method is validated in two scenarios: 1) population evolution and 2) urban travel demand analysis. Experimental results demonstrate that compared with the machine-learning surrogate and evolutionary optimization, our method can achieve higher accuracies with much lower computational complexities.
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Generalized multigranulation sequential three-way decision models for hierarchical classification. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.10.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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11
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Modeling relationships in three-way conflict analysis with subsethood measures. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.110131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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12
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An Improved Three-Way K-Means Algorithm by Optimizing Cluster Centers. Symmetry (Basel) 2022. [DOI: 10.3390/sym14091821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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 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.
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He SF, Wang YM, Pan X, Chin KS. A novel behavioral three-way decision model with application to the treatment of mild symptoms of COVID-19. Appl Soft Comput 2022; 124:109055. [PMID: 35637858 PMCID: PMC9132434 DOI: 10.1016/j.asoc.2022.109055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 04/19/2022] [Accepted: 05/16/2022] [Indexed: 11/18/2022]
Abstract
The Coronavirus Disease 2019 (COVID-19) has popularized since late December 2019. In present, it is still highly transmissible and has severe impact on the public health and global economy. Due to the lack of specific drug and the appearance of different variants, the selection of the antiviral therapy to treat the patients with mild symptom is of vital importance. Hence, in this paper, we propose a novel behavioral Three-Way Decision (3WD) model and apply it to the medicine selection decision. First, a new relative utility function is constructed by considering the risk-aversion behavior and regret-aversion behavior of human beings. Second, based on the relative utility function, some new rules are defined to calculate the thresholds and conditional probabilities in 3WD and some corresponding theorems are explored and proved. Next, a new information fusion mechanism in the framework of evidential reasoning algorithm is developed. Then, the decision results are obtained based on the Bayesian decision procedure and the principle of maximum utility. Finally, an example with large-scale data set and an example about medicine selection for COVID-19 are provided to show the implementation process and effectiveness of the proposed method. Comparative analysis and sensitivity analysis are also performed to illustrate the superiority and the robustness of the current proposal.
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Affiliation(s)
- Shi-Fan He
- Decision Sciences Institute, Fuzhou University, Fujian, 350108, China
- Department of Systems Engineering and Engineering Management, City University of Hong Kong, Hong Kong
| | - Ying-Ming Wang
- Decision Sciences Institute, Fuzhou University, Fujian, 350108, China
| | - Xiaohong Pan
- Decision Sciences Institute, Fuzhou University, Fujian, 350108, China
- Department of Systems Engineering and Engineering Management, City University of Hong Kong, Hong Kong
| | - Kwai-Sang Chin
- Department of Systems Engineering and Engineering Management, City University of Hong Kong, Hong Kong
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14
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Uncertainty measurement for a gene space based on class-consistent technology: an application in gene selection. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03657-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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15
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16
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Feature selection using self-information uncertainty measures in neighborhood information systems. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03760-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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17
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Yang C, Ge H, Xu Y. Incremental maintenance of three-way regions with variations of objects and values in hybrid incomplete decision systems. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03736-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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18
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19
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The updating methods of object-induced three-way concept in dynamic formal contexts. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03646-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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20
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Wang J, Ma X, Xu Z, Zhan J. A three-way decision approach with risk strategies in hesitant fuzzy decision information systems. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2021.12.079] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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21
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22
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Peng J, Cai Y, Xia G, Hao M. Three-way decision theory based on interval type-2 fuzzy linguistic term sets. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-213236] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
This study examines decision theory based on interval type-2 fuzzy sets with linguistic information for the three-way decision approach by addressing the challenge of uncertainty for information analysis and fusion in subjective decision-making processes. First, the interval type-2 fuzzy linguistic term sets (IT2 FLTSs) are defined to represent and normalize the uncertain preference information in linguistic decision-making. Subsequently, perception computing based on computing with words paradigm is introduced to implement information fusion among different decision-makers in the linguistic information-based fuzzy logic reasoning process. Then, a three-way decision (3WD) theory based on IT2 FLTSs with fuzzy neighborhood covering is proposed, and the corresponded tri-partitioning strategies that satisfy Jaccard similarity of membership distributions are given. Finally, 3WD theory is applied to multi-criteria group decision-making with linguistic terms, and the algorithm steps are illustrated by a promising application under the background of coronavirus disease 2019 to reveal the feasibility and practicability of the proposed approach.
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Affiliation(s)
- Jiangang Peng
- Intelligent Manufacturing Institute, Hefei University of Technology, Hefei, China
| | - Ya Cai
- Intelligent Manufacturing Institute, Hefei University of Technology, Hefei, China
| | - Guang Xia
- Institute of Automotive Engineering, Hefei University of Technology, Hefei, China
| | - Ming Hao
- Intelligent Manufacturing Institute, Hefei University of Technology, Hefei, China
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23
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Xu W, Yuan K, Li W. Dynamic updating approximations of local generalized multigranulation neighborhood rough set. APPL INTELL 2022. [DOI: 10.1007/s10489-021-02861-x] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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24
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Ye P, Wang X, Xiong G, Chen S, Wang FY. TiDEC: A Two-Layered Integrated Decision Cycle for Population Evolution. IEEE TRANSACTIONS ON CYBERNETICS 2021; 51:5897-5906. [PMID: 31945004 DOI: 10.1109/tcyb.2019.2957574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Agent-based simulation is a useful approach for the analysis of dynamic population evolution. In this field, the existing models mostly treat the migration behavior as a result of utility maximization, which partially ignores the endogenous mechanisms of human decision making. To simulate such a process, this article proposes a new cognitive architecture called the two-layered integrated decision cycle (TiDEC) which characterizes the individual's decision-making process. Different from the previous ones, the new hybrid architecture incorporates deep neural networks for its perception and implicit knowledge learning. The proposed model is applied in China and U.S. population evolution. To the best of our knowledge, this is the first time that the cognitive computation is used in such a field. Computational experiments using the actual census data indicate that the cognitive model, compared with the traditional utility maximization methods, cannot only reconstruct the historical demographic features but also achieve better prediction of future evolutionary dynamics.
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Ju H, Ding W, Yang X, Fujita H, Xu S. Robust supervised rough granular description model with the principle of justifiable granularity. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107612] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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28
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A comprehensive model and computational methods to improve Situation Awareness in Intelligence scenarios. APPL INTELL 2021; 51:6585-6608. [PMID: 34764614 PMCID: PMC8325623 DOI: 10.1007/s10489-021-02673-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/09/2021] [Indexed: 11/01/2022]
Abstract
This paper presents a comprehensive model for representing and reasoning on situations to support decision makers in Intelligence analysis activities. The main result presented in the paper stems from a work of refinement and abstraction of previous results of the authors related to the use of Situation Awareness and Granular Computing for the development of analysis methods and techniques to support Intelligence. This work made it possible to derive the characteristics of the model from previous case studies and applications with real data, and to link the reasoning techniques to concrete approaches used by intelligence analysts such as, for example, the Structured Analytic Techniques. The model allows to represent an operational situation according to three complementary perspectives: descriptive, relational and behavioral. These three perspectives are instantiated on the basis of the principles and methods of Granular Computing, mainly based on the theories of fuzzy and rough sets, and with the help of further structures such as graphs. As regards the reasoning on the situations thus represented, the paper presents four methods with related case studies and applications validated on real data.
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30
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Double-quantitative rough sets, optimal scale selection and reduction in multi-scale dominance IF decision tables. Int J Approx Reason 2021. [DOI: 10.1016/j.ijar.2020.12.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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31
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Zhu X, Pedrycz W, Li Z. A Development of Granular Input Space in System Modeling. IEEE TRANSACTIONS ON CYBERNETICS 2021; 51:1639-1650. [PMID: 30892261 DOI: 10.1109/tcyb.2019.2899633] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
In this paper, we elaborate on a new design approach to the development and analysis of granular input spaces and ensuing granular modeling. Given a numeric model (no matter what specific design methodology has been used to construct it and what architecture has been adopted), we form a granular input space through allocating a certain level of information granularity across the input variables. The formation of granular input space helps us gain a better insight into the ranking of input variables with respect to their precision (the variables with a lower level of information granularity need to be specified in a precise way when estimating the inputs). As a consequence, for granular inputs, the outputs of the granular model are also information granules (say, intervals, fuzzy sets, rough sets, etc.). It is shown that the process of forming granular input space can be sought as an optimization of allocation of information granularity across the input variables so that the specificity of the corresponding granular outputs of the granular model becomes the highest while coverage of data becomes maximized. The construction of granular input space dwells upon two fundamental principles of granular computing-the principle of justifiable granularity and the optimal allocation of information granularity. The quality of the granular input space is quantified in terms of the two conflicting criteria, that is, the specificity of the results produced by the granular model and the coverage of experimental data delivered by this model. In the ensuing optimization problem, one maximizes a product of specificity and coverage. Differential evolution is engaged in this optimization task. The experimental studies involve both synthetic dataset and data coming from the machine learning repository.
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Xue Y, Deng Y. Decision making under measure-based granular uncertainty with intuitionistic fuzzy sets. APPL INTELL 2021; 51:6224-6233. [PMID: 34764583 PMCID: PMC7862861 DOI: 10.1007/s10489-021-02216-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/13/2021] [Indexed: 10/29/2022]
Abstract
Yager has proposed the decision making under measure-based granular uncertainty, which can make decision with the aid of Choquet integral, measure and representative payoffs. The decision making under measure-based granular uncertainty is an effective tool to deal with uncertain issues. The intuitionistic fuzzy environment is the more real environment. Since the decision making under measure-based granular uncertainty is not based on intuitionistic fuzzy environment, it cannot effectively solve the decision issues in the intuitionistic fuzzy environment. Then, when the issues of decision making are under intuitionistic fuzzy environment, what is the decision making under measure-based granular uncertainty with intuitionistic fuzzy sets is still an open issue. To deal with this kind of issues, this paper proposes the decision making under measure-based granular uncertainty with intuitionistic fuzzy sets. The decision making under measure-based granular uncertainty with intuitionistic fuzzy sets can effectively solve the decision making issues in the intuitionistic fuzzy environment, in other words, it can extend the decision making under measure-based granular uncertainty to the intuitionistic fuzzy environment. Numerical examples are applied to verify the validity of the decision making under measure-based granular uncertainty with intuitionistic fuzzy sets. The experimental results demonstrate that the decision making under measure-based granular uncertainty with intuitionistic fuzzy sets can represent the objects successfully and make decision effectively. In addition, a practical application of applied intelligence is used to compare the performance between the proposed model and the decision making under measure-based granular uncertainty. The experimental results show that the proposed model can solve some decision problems that the decision making under measure-based granular uncertainty cannot solve.
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Affiliation(s)
- Yige Xue
- Institute of Fundamental and Frontier Science, University of Electronic Science and Technology of China, Chengdu, 610054 China
| | - Yong Deng
- Institute of Fundamental and Frontier Science, University of Electronic Science and Technology of China, Chengdu, 610054 China
- School of Eduction, Shaanxi Normal University, Xi’an, 710062 China
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33
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Liu Z, He X, Deng Y. Network-based evidential three-way theoretic model for large-scale group decision analysis. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2020.08.042] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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34
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A novel similarity measure for spatial entity resolution based on data granularity model: Managing inconsistencies in place descriptions. APPL INTELL 2021. [DOI: 10.1007/s10489-020-01959-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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35
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A method based on Graph Theory and Three Way Decisions to evaluate critical regions in epidemic diffusion:: An analysis of COVID-19 in Italy. APPL INTELL 2021; 51:2939-2955. [PMID: 34764578 PMCID: PMC7808933 DOI: 10.1007/s10489-020-02173-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/22/2020] [Indexed: 11/11/2022]
Abstract
The paper reports the results of an analysis of COVID-19 diffusion in Italy. The analysis was carried out with a new method based on the combined use of a 3 Way Decisions model and graph theory. Specifically, the data about infected people in the Italian regions is assessed by means of an evaluation function which allows the tri-partitioning of Italy and the identification of high, medium or low critical regions. The tri-partition is performed, along the temporal evolution of the COVID-19 diffusion, by calculating two threshold values which take into account the containment actions that, from time to time, the decision makers have implemented. The effects of a containment action are related to a reduction in the centrality value of a region. To estimate the effect of containment actions, we evaluated two approaches. The first is based on a uniform reduction in the centrality values of the regions, the second estimates the effects of containment actions starting from the mobility changes data provided by the Google Community Mobility reports. The results of our evaluation based on real data of the COVID-19 diffusion in Italy are encouraging and represent a good starting point for future extensions of the method.
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37
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Zhang Q, Huang Z, Wang G. A novel sequential three-way decision model with autonomous error correction. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2020.106526] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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38
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Partial-overall dominance three-way decision models in interval-valued decision systems. Int J Approx Reason 2020. [DOI: 10.1016/j.ijar.2020.08.014] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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39
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40
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41
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42
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Jiang Z, Dou H, Song J, Wang P, Yang X, Qian Y. Data-guided multi-granularity selector for attribute reduction. APPL INTELL 2020. [DOI: 10.1007/s10489-020-01846-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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Multiple attribute group decision making based on nucleolus weight and continuous optimal distance measure. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2020.105719] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Tang G, Chiclana F, Lin X, Liu P. Interval type-2 fuzzy multi-attribute decision-making approaches for evaluating the service quality of Chinese commercial banks. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2019.105438] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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On rule acquisition methods for data classification in heterogeneous incomplete decision systems. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2020.105472] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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