AN APPLICATION OF FEATURE SELECTION FOR THE FUZZY RULE BASED CLASSIFIER DESIGN BASED ON AN ENLARGED HEDGE ALGEBRAS FOR HIGH-DIMENSIONAL DATASETS

Pham Dinh 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 in [12]. As the other methods, a difficult problem 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 proposed in [12] by utilizing the feature selection algorithm proposed in [19]. 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.

Keywords


Hedge Algebras; Fuzzy Classification System; Feature Selection; High-dimensional Dataset

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DOI: https://doi.org/10.15625/0866-708X/53/5/5805 Display counter: Abstract : 30 views. PDF : 19 views.

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Published by Vietnam Academy of Science and Technology