The fuzzy rule based classifier (FRBC) design methods have intensively been being studied during last years. The ones designed by utilizing hedge algebras as a formalism to generate the optimal linguistic values along with their (triangular and trapezoidal) fuzzy sets based semantics for the FRBCs have been proposed. Those design methods generate the fuzzy sets based semantics because the classification reasoning method still bases on the fuzzy set theory. One question which has been arisen is whether there is a pure hedge algebras classification reasoning method so that the fuzzy sets based semantic of the linguistic values in the fuzzy rule bases can be replaced with the hedge algebras based semantic. This paper answers that question by presenting a fuzzy rule based classifier design method based on hedge algebras with a pure hedge algebras classification reasoning method. The experimental results over 17 real world datasets are compared to the existing methods based on hedge algebras and fuzzy sets theory showing that the proposed method is effective and produces good results.
A HEDGE ALGEBRAS BASED REASONING METHOD FOR FUZZY RULE BASED CLASSIFIER
08.10.2019
doi:10.15625/2525-2518/57/5/13811
Vietnam Journal of Science and Technology; Vol 57, No 5 (2019); 631 ; Tạp chí Khoa học và Công nghệ; Vol 57, No 5 (2019); 631 ; 2525-2518
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
DDC: | 629 |
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