Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory: foundations and applications
Fuzzy set theory: foundations and applications
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
The role of fuzzy logic in the management of uncertainty in expert systems
Fuzzy Sets and Systems
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Fuzzy classification has become of great interest because of its ability to utilize simple linguistically interpretable rules and has overcome the limitations of symbolic or crisp rule based classifiers. This paper introduces an extension to fuzzy classifier: a neutrosophic classifier, which would utilize neutrosophic logic for its working. Neutrosophic logic is a generalized logic that is capable of effectively handling indeterminacy, stochasticity acquisition errors that fuzzy logic cannot handle. The proposed neutrosophic classifier employs neutrosophic logic for its working and is an extension of commonly used fuzzy classifier. It is compared with the commonly used fuzzy classifiers on the following parameters: nature of membership functions, number of rules and indeterminacy in the results generated. It is proved in the paper that extended fuzzy classifier: neutrosophic classifier; optimizes the said parameters in comparison to the fuzzy counterpart. Finally the paper is concluded with justifying that neutrosophic logic though in its nascent stage still holds the potential to be experimented for further exploration in different domains.