@misc{10481/109374,
year = {2023},
month = {2},
url = {https://hdl.handle.net/10481/109374},
abstract = {The fuzzy rule-based classification system (FRBCS) is a popular tool for classification problems due to its interpretability and comprehensibility. As an extension of fuzzy numbers, the concept of Z-number is a more appropriate formal structure to describe uncertain and partially reliable information. A Z-number is an ordered pair of fuzzy numbers, where the second fuzzy number describes the reliability of the first one. As a result of its representation capability, it can receive better classification results. However, there is still a gap in the application of Z-numbers to classification problems due to their high computation complexity. To take advantage of the Z-number, we design a simple way to make Z-numbers apply to classification problems. Use the second fuzzy number to adjust the first fuzzy number to fit the training data. Then we create a kind of Z-number-valued if–then rule by extending the fuzzy if–then rule. In addition, a Z-number-valued rule-based classification system (ZRBCS) is developed, including two main processes: rule generation and new pattern classification. The developed system can cover more information than the classic fuzzy rule-based system, which can improve classification effects. The proposed ZRBCS is compared with classical FRBCS with/without certain degrees and three classical classification algorithms. According to statistical tests, ZRBCS is superior to FRBCS and two other algorithms.},
organization = {FEDER 2014–2020 and the Regional Ministry of Economy, Knowledge, Enterprise and Universities (CECEU) of Andalusia, B-TIC-590-UGR20},
organization = {China Scholarship Council (CSC)},
organization = {MCIN/AEI, PID2019-103880RB-I00},
organization = {Andalusian government, P20_00673},
publisher = {Elsevier},
keywords = {Z-number},
keywords = {Fuzzy modelling},
keywords = {Z-number-valued if-then rule},
title = {Z-number-valued Rule-Based Classification System},
doi = {10.1016/j.asoc.2023.110168},
author = {Li, Yangxue and Herrera Viedma, Enrique and Pérez Gálvez, Ignacio Javier and Barragán Guzmán, Mónica and Morente Molinera, Juan Antonio},
}