A hedge algebras based reasoning method for fuzzy rule based classifier

Pham Dinh Phong; Nguyen Duc Du; Hoang Van Thong

doi:10.15625/2525-2518/57/5/13811

Author affiliations

Authors

Pham Dinh Phong Faculty of Information Technology, University of Transport and Communications, No. 3, Cau Giay street, Dong Da district, Ha Noi, Viet Nam
Nguyen Duc Du Faculty of Information Technology, University of Transport and Communications, No. 3, Cau Giay street, Dong Da district, Ha Noi, Viet Nam
Hoang Van Thong Faculty of Information Technology, University of Transport and Communications, No. 3, Cau Giay street, Dong Da district, Ha Noi, Viet Nam

DOI:

https://doi.org/10.15625/2525-2518/57/5/13811

Keywords:

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

Abstract

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.

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