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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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Fang Y, Zhou D, Li K, Ju Z, Liu H. Attribute-Driven Granular Model for EMG-Based Pinch and Fingertip Force Grand Recognition. IEEE TRANSACTIONS ON CYBERNETICS 2021; 51:789-800. [PMID: 31425131 DOI: 10.1109/tcyb.2019.2931142] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Fine multifunctional prosthetic hand manipulation requires precise control on the pinch-type and the corresponding force, and it is a challenge to decode both aspects from myoelectric signals. This paper proposes an attribute-driven granular model (AGrM) under a machine-learning scheme to solve this problem. The model utilizes the additionally captured attribute as the latent variable for a supervised granulation procedure. It was fulfilled for EMG-based pinch-type classification and the fingertip force grand prediction. In the experiments, 16 channels of surface electromyographic signals (i.e., main attribute) and continuous fingertip force (i.e., subattribute) were simultaneously collected while subjects performing eight types of hand pinches. The use of AGrM improved the pinch-type recognition accuracy to around 97.2% by 1.8% when constructing eight granules for each grasping type and received more than 90% force grand prediction accuracy at any granular level greater than six. Further, sensitivity analysis verified its robustness with respect to different channel combination and interferences. In comparison with other clustering-based granulation methods, AGrM achieved comparable pinch recognition accuracy but was of lowest computational cost and highest force grand prediction accuracy.
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Cheng Y, Zhang Q, Wang G. Optimal scale combination selection for multi-scale decision tables based on three-way decision. INT J MACH LEARN CYB 2020. [DOI: 10.1007/s13042-020-01173-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Novel classes of coverings based multigranulation fuzzy rough sets and corresponding applications to multiple attribute group decision-making. Artif Intell Rev 2020. [DOI: 10.1007/s10462-020-09846-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Zhang L, Zhan J, Xu Z, Alcantud JCR. Covering-based general multigranulation intuitionistic fuzzy rough sets and corresponding applications to multi-attribute group decision-making. Inf Sci (N Y) 2019. [DOI: 10.1016/j.ins.2019.04.054] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Zhan J, Sun B, Alcantud JCR. Covering based multigranulation(I,T)-fuzzy rough set models and applications in multi-attribute group decision-making. Inf Sci (N Y) 2019. [DOI: 10.1016/j.ins.2018.10.016] [Citation(s) in RCA: 113] [Impact Index Per Article: 22.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Qi J, Wei L, Wan Q. Multi-level granularity in formal concept analysis. GRANULAR COMPUTING 2018. [DOI: 10.1007/s41066-018-0112-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Li J, Huang C, Qi J, Qian Y, Liu W. Three-way cognitive concept learning via multi-granularity. Inf Sci (N Y) 2017. [DOI: 10.1016/j.ins.2016.04.051] [Citation(s) in RCA: 230] [Impact Index Per Article: 32.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Wang D, Pedrycz W, Li Z. Design of granular interval-valued information granules with the use of the principle of justifiable granularity and their applications to system modeling of higher type. Soft comput 2015. [DOI: 10.1007/s00500-015-1904-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Li J, Mei C, Xu W, Qian Y. Concept learning via granular computing: A cognitive viewpoint. Inf Sci (N Y) 2014; 298:447-467. [PMID: 32226109 PMCID: PMC7094283 DOI: 10.1016/j.ins.2014.12.010] [Citation(s) in RCA: 166] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2014] [Revised: 11/16/2014] [Accepted: 12/03/2014] [Indexed: 10/27/2022]
Abstract
Concepts are the most fundamental units of cognition in philosophy and how to learn concepts from various aspects in the real world is the main concern within the domain of conceptual knowledge presentation and processing. In order to improve efficiency and flexibility of concept learning, in this paper we discuss concept learning via granular computing from the point of view of cognitive computing. More precisely, cognitive mechanism of forming concepts is analyzed based on the principles from philosophy and cognitive psychology, including how to model concept-forming cognitive operators, define cognitive concepts and establish cognitive concept structure. Granular computing is then combined with the cognitive concept structure to improve efficiency of concept learning. Furthermore, we put forward a cognitive computing system which is the initial environment to learn composite concepts and can integrate past experiences into itself for enhancing flexibility of concept learning. Also, we investigate cognitive processes whose aims are to deal with the problem of learning one exact or two approximate cognitive concepts from a given object set, attribute set or pair of object and attribute sets.
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Affiliation(s)
- Jinhai Li
- Faculty of Science, Kunming University of Science and Technology, Kunming 650500, Yunnan, PR China
| | - Changlin Mei
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, PR China
| | - Weihua Xu
- School of Mathematics and Statistics, Chongqing University of Technology, Chongqing 400054, PR China
| | - Yuhua Qian
- Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, School of Computer and Information Technology, Shanxi University, Taiyuan 030006, Shanxi, PR China
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Human-centric analysis and interpretation of time series: a perspective of granular computing. Soft comput 2014. [DOI: 10.1007/s00500-013-1213-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Pedrycz W, Homenda W. Building the fundamentals of granular computing: A principle of justifiable granularity. Appl Soft Comput 2013. [DOI: 10.1016/j.asoc.2013.06.017] [Citation(s) in RCA: 194] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Granular type-2 membership functions: A new approach to formation of footprint of uncertainty in type-2 fuzzy sets. Appl Soft Comput 2013. [DOI: 10.1016/j.asoc.2013.03.007] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Pedrycz W, Russo B, Succi G. Knowledge transfer in system modeling and its realization through an optimal allocation of information granularity. Appl Soft Comput 2012. [DOI: 10.1016/j.asoc.2012.02.004] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Pedrycz W, Bargiela A. An Optimization of Allocation of Information Granularity in the Interpretation of Data Structures: Toward Granular Fuzzy Clustering. ACTA ACUST UNITED AC 2012; 42:582-90. [DOI: 10.1109/tsmcb.2011.2170067] [Citation(s) in RCA: 186] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Leite D, Ballini R, Costa P, Gomide F. Evolving fuzzy granular modeling from nonstationary fuzzy data streams. EVOLVING SYSTEMS 2012. [DOI: 10.1007/s12530-012-9050-9] [Citation(s) in RCA: 96] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Pedrycz W. Human Centricity and Perception-Based Perspective and Their Centrality to the Agenda of Granular Computing. INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2011. [DOI: 10.4018/jcini.2011100104] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In spite of their striking diversity, numerous tasks and architectures of intelligent systems such as those permeating multivariable data analysis, decision-making processes along with their underlying models, recommender systems and others exhibit two evident commonalities. They promote (a) human centricity and (b) vigorously engage perceptions (rather than plain numeric entities) in the realization of the systems and their further usage. Information granules play a pivotal role in such settings. Granular Computing delivers a cohesive framework supporting a formation of information granules and facilitating their processing. The author exploits two essential concepts of Granular Computing. The first one deals with the construction of information granules. The second one helps endow constructs of intelligent systems with a much needed conceptual and modeling flexibility. The study elaborates in detail on the three representative studies. In the first study being focused on the Analytic Hierarchy Process (AHP) used in decision-making, the author shows how an optimal allocation of granularity helps improve the quality of the solution and facilitate collaborative activities in models of group decision-making. The second study is concerned with a granular interpretation of temporal data where the role of information granularity is profoundly visible when effectively supporting human centric description of relationships existing in data. The third study concerns a formation of granular logic descriptors on a basis of a family of logic descriptors.
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Affiliation(s)
- Witold Pedrycz
- University of Alberta, Canada, and Polish Academy of Sciences, Poland
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Pedrycz W. The Principle of Justifiable Granularity and an Optimization of Information Granularity Allocation as Fundamentals of Granular Computing. JOURNAL OF INFORMATION PROCESSING SYSTEMS 2011. [DOI: 10.3745/jips.2011.7.3.397] [Citation(s) in RCA: 55] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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Qian Y, Liang J, Dang C. Knowledge structure, knowledge granulation and knowledge distance in a knowledge base. Int J Approx Reason 2009. [DOI: 10.1016/j.ijar.2008.08.004] [Citation(s) in RCA: 150] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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