Toward Z-Number-Based Classification of Dataset - Aliyev, Rashad R.; Huseynov, Oleg H.; Guirimov, Babek Ghalib (Springer Science and Business Media Deutschland GmbH, 2023)
Nowadays, a lot of classification techniques including probabilistic and fuzzy methods exist. The works devoted to dealing with fusion of probabilistic and fuzzy uncertainties of information are scarce. In view of this, partial reliability of information that stems from uncertainty and complexity of real datasets is of interest. Prof. Zadeh introduced a concept of Z-number to describe reliability of information under fuzziness and probabilistic uncertainty. In this work, an approach to Z-number-valued classification of dataset is outlined. The is aim is to describe partial reliability of knowledge expressed by classification. A benchmark data set is used is considered to illustrate the proposed approach. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
15th International Conference on Application of Fuzzy Systems, Soft Computing and Artificial Intelligence Tools, ICAFS 2022 -- 2022-08-26 through 2022-08-27 -- Budva -- 291409
Keywords: Classification; Clustering; Fuzzy set; Reliability; Z-number