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

Authors

  • Pham Dinh Phong Công ty Prévoir Việt Nam

DOI:

https://doi.org/10.15625/0866-708X/53/5/5805

Keywords:

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

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.

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Published

2015-10-12

Issue

Section

Articles