Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.
Uncertainty is unavoidable when dealing with data. The errors in measurements, limitations of measuring tools, or imprecise definition of linguistic variables may result in different types of uncertainty. These ambiguities may be due to vagueness in data which results from the imprecise boundaries of data sets; inconsistency that reflects conflict and contradiction between sets; qualitative description of data which sometimes taken by expertise; or some other type. Ignoring dealing with these types of uncertainty affects the reliability of research and the validity of the results.
This article presents three approaches to treat uncertainty using fuzzy logic, intuitionistic logic, and neutrosophic logic and their methodologies in treating these kinds of ambiguity. Fuzzy logic and neutrosophic logic are used in building Rule-based Classification Systems. Different comparisons are presented to illustrate the importance of choosing the suitable logic to tackle the uncertainty in different data sets. These approaches are applied on six real world data sets; Iris, Wine, Wisconsin Diagnostic Breast Cancer, Seeds, Pima, and Statlog (Heart); which are available on UCI Machine Learning Repository web site. The results show that the type of uncertainty in the data set plays a great role in choosing the appropriate logic.
