Toward Z-Number-Based Classification of Dataset

Abstract

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.