M-CFIS-R: Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing
Article
Article Title | M-CFIS-R: Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing |
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ERA Journal ID | 213646 |
Article Category | Article |
Authors | Tuan, Tran Manh, Lan, Luong Thi Hong, Chou, Shuo-Yan, Ngan, Tran Thi, Son, Le Hoang, Giang, Nguyen Long and Ali, Mumtaz |
Journal Title | Mathematics |
Journal Citation | 8 (5), pp. 1-24 |
Article Number | 707 |
Number of Pages | 24 |
Year | May 2020 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 2227-7390 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/math8050707 |
Web Address (URL) | https://www.mdpi.com/2227-7390/8/5/707 |
Abstract | Complex fuzzy theory has strong practical background in many important applications, especially in decision-making support systems. Recently, the Mamdani Complex Fuzzy Inference System (M-CFIS) has been introduced as an effective tool for handling events that are not restricted to only values of a given time point but also include all values within certain time intervals (i.e., the phase term). In such decision-making problems, the complex fuzzy theory allows us to observe both the amplitude and phase values of an event, thus resulting in better performance. However, one of the limitations of the existing M-CFIS is the rule base that may be redundant to a specific dataset. In order to handle the problem, we propose a new Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing called M-CFIS-R. Several fuzzy similarity measures such as Complex Fuzzy Cosine Similarity Measure (CFCSM), Complex Fuzzy Dice Similarity Measure (CFDSM), and Complex Fuzzy Jaccard Similarity Measure (CFJSM) together with their weighted versions are proposed. Those measures are integrated into the M-CFIS-R system by the idea of granular computing such that only important and dominant rules are being kept in the system. The difference and advantage of M-CFIS-R against M-CFIS is the usage of the training process in which the rule base is repeatedly changed toward the original base set until the performance is better. By doing so, the new rule base in M-CFIS-R would improve the performance of the whole system. Experiments on various decision-making datasets demonstrate that the proposed M-CFIS-R performs better than M-CFIS. |
Keywords | complex fuzzy set; similarity measure; complex fuzzy measure; Mamdani Complex Fuzzy Inference System (M-CFIS); rule reduction; granular computing |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460204. Fuzzy computation |
Byline Affiliations | Graduate University of Science and Technology, Vietnam |
Thuyloi University, Vietnam | |
Vietnam Academy of Science and Technology, Vietnam | |
National Taiwan University of Science and Technology, Taiwan | |
Vietnam National University, Vietnam | |
Deakin University |
https://research.usq.edu.au/item/w28y2/m-cfis-r-mamdani-complex-fuzzy-inference-system-with-rule-reduction-using-complex-fuzzy-measures-in-granular-computing
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