An application of feature selection for the fuzzy rule based classifier design besed on an enlarged hedge algebras for high-dimesional datasets

  • Phạm Đình Phong

Abstract

The fuzzy rule based classification system (FRBCS) design methods, whose fuzzy rules are in the form of if-then sentences, have been being studied intensively during last years. One of the eminent FRBCS design methods utilizing an enlarged hedge algebras as a formal mechanism to design optimal linguistic terms integrated with their trapezoidal fuzzy sets has been proposed by Ho N. c. et. ai. As the other methods, an entanglement of this approach needed to be solved is dealing with the high-dimensional and multi-instance datasets. This paper presents an approach to tackle the high-dimensional dataset problem for the FRBCS design method based on an enlarged hedge algebras by utilizing the feature selection algorithm proposed by Sun X. et. al. The experimental results over 8 high-dimensional datasets have shown that the proposed method allows saving much execution time than the original one, but retains the equivalent classification performance as well as the equivalent FRBCS complexity.

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Published
2017-10-06
Section
Articles