Phạm Đình Phong, Nguyễn Đức Dư, Hoàng Văn Thông


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.


fuzzy rule based classifier, hedge algebras, fuzziness measure, fuzziness intervals, semantically quantifying mapping value

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DOI: https://doi.org/10.15625/2525-2518/57/5/13811 Display counter: Abstract : 522 views. PDF : 133 views.


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