Design of rule-based models through information granulation |
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| Affiliation: | 1. CAPES Foundation, Ministry of Education of Brazil, Brasília – DF, 70040-020, Brazil;2. Universidade Estadual Paulista “Júlio de Mesquita Filho”, Rua Cristóvão Colombo, 2265, 15054-000, S. J. do Rio Preto, Brazil;3. Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465, Porto, Portugal;1. School of Business, Shanxi Datong University, Datong 037009, PR China;2. Dongling School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, PR China;3. Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, PR China;4. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, PR China\n;1. Division of Data Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam;2. Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam;3. College of Information and Communication Technology, Thai Nguyen University, Quyet Thang, Thai Nguyen, Vietnam;1. Informatics Coordination, Instituto Federal do Espírito Santo (Campus Serra), ES 010 highway, km 6.5, 29173087 Manguinhos, Serra, Espírito Santo, Brazil;2. Deparment of Informatics, Universidade Federal do Espírito Santo (Campus Vitória), 514 Fernando Ferrari Avenue, 29075910 Goabeiras, Vitória, Espírito Santo, Brazil;3. Department of Computing and Electronics, Universidade Federal do Espírito Santo (Campus São Mateus), BR 101 Norte highway, km 60, 29932540 Bairro Litorâneo, São Mateus, Espírito Santo, Brazil;4. Autonomous and Industrial Robotics Research Group (GRAI), Department of Electronic Engineering, Universidad Técnica Federico Santa María, 1680 España Avenue, Casilla 110-V, Valparaíso, Chile;1. Department of Literature and Languages, Texas A&M University-Commerce, TX, USA;2. Department of Computer Science and Information Systems, Texas A&M University-Commerce, TX, USA;3. Department of Health Management and Informatics, University of Central Florida, FL, USA\n |
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| Abstract: | In this study, we explore the combination of two well defined topics in fuzzy systems research: fuzzy rule based systems, and information granulation. Rule based systems are a powerful and well-studied form of knowledge representation, due to their approximation abilities and interpretability. In recent years, these types of systems have become increasingly powerful with regards to modeling accuracy; however, many of these improvements come at the cost of model interpretability. This recent direction of research has left an unexplored avenue towards the generation of increasingly interpretable fuzzy rule based models, which we intend to explore. Information granulation is a relatively new, yet very promising area of research in human centric systems. As a form of knowledge representation, information granulation is very well suited to fuzzy rule based systems, where rules represent linguistic quantities in a, intuitively understandable format. It is notable that the combination of these two concepts has been left largely unstudied. We aim to explore this union by defining a methodology for the construction of a partially granular fuzzy rule based model. The aim of this novel model format is to provide a first step in the improvement of fuzzy model interpretability, through the use of information granulation. We are additionally interested in studying new ways of generating fuzzy rules; hence, we will also look at the use of hierarchical clustering as a potential alternative to the tried and tested Fuzzy C Means clustering algorithm. The models created using hierarchical clustering are then compared with those generated using Fuzzy C Means to evaluate the effectiveness of this algorithm. As a result of these experiments, we demonstrate that partially granular fuzzy rules are capable of providing a significant improvement to fuzzy rule interpretability, and we believe that granular fuzzy models present an exciting avenue of future research in human centric systems. |
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