Abstract
This review paper of fuzzy classifiers with improved interpretability and accuracy parameter discussed the most fundamental aspect of very effective and powerful tools in form of probabilistic reasoning, The fuzzy logic concept allows the effective realization of ap-proximate, vague, uncertain, dynamic, and more realistic conditions, which is closer to the actual physical world and human thinking. The fuzzy theory has the competency to catch the lack of preciseness of linguistic terms in a speech of natural language. The fuzzy theory provides a more significant competency to model humans like com-mon-sense reasoning and conclusion making to fuzzy set and rules as good membership functions. Also, in this paper reviews discussed the evaluation of the fuzzy set, type-1, type-2, and interval type-2 fuzzy system from traditional Boolean crisp set logic along with interpretability and accuracy issues in the fuzzy system.
Highlights
Fuzzy logic, Fuzzy set, Crisp sets, Accuracy and Interpretability trade off, Type-2 fuzzy system, Interval type-2 fuzzy system
The acceptance of Type-2 fuzzy sets is very broad in Rule-Based Fuzzy Systems (RBFSs) because they handled uncertainties be modeled by them while such uncertainties cannot be handled by type-1 fuzzy sets
Interval type-2 fuzzy systems have shown the benefits of type-2 fuzzy, basically extended from the concepts of traditional type-1 systems with reduced computational cost and complexity
Summary
Interpretability talks about the proficiency of the fuzzy model to explain the behavior of the system understandably. Interpretability is the non-objective property depending on the following properties: i. Shapes of fuzzy sets Several criteria are i. ISSN (Online) : 2582-7006 International Conference on Artificial Intelligence (ICAI-2021). Journal of Informatics Electrical and Electronics Engineering (JIEEE) A2Z Journals
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