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An application of feature selection for the fuzzy rule based classifier design with the order based semantics of linguistic terms for high dimensional datasets

The fuzzy rule based classification system (FRBCS) design methods, whose fuzzy rules are in the form of if-then sentences, have been under intensive study during last years. The fuzzy rule based classification system (FRBThis paper presents an approach to tackle the high-dimensional dataset problem for the hedge algebras based classification method proposed in by utilizing the feature selection algorithm proposed inS) design methods, whose fuzzy rules are in the form of if-then sentences, have been under intensive study during last years. | Journal of Computer Science and Cybernetics, V.31, N.2 (2015), 171–184 DOI: 10.15625/1813-9663/31/2/5025 AN APPLICATION OF FEATURE SELECTION FOR THE FUZZY RULE BASED CLASSIFIER DESIGN WITH THE ORDER BASED SEMANTICS OF LINGUISTIC TERMS FOR HIGH-DIMENSIONAL DATASETS PHAM DINH PHONG1,2 1 Pr´voir e 2 Ph.D. Vietnam, 23 Phan Chu Trinh, Hanoi, Vietnam student, University of Engineering and Technology, Hanoi National University Abstract. The fuzzy rule based classification system (FRBCS) design methods, whose fuzzy rules are in the form of if-then sentences, have been under intensive study during last years. One of the outstanding FRBCS design methods utilizing hedge algebras as a mathematical formalism is proposed in [9]. As in other methods, a difficult problem with the high-dimensional and multi-instance datasets needs to be solved. This paper presents an approach to tackle the high-dimensional dataset problem for the hedge algebras based classification method proposed in [9] by utilizing the feature selection algorithm proposed in [20]. The experimental results over eight high-dimensional datasets have shown that the proposed method saves much execution time than the original one, while retaining the equivalent classification performance as well as the equivalent FRBCS complexity. The proposed method is also compared with three classical classification methods based on the statistical and probabilistic approaches showing that it is a robust classifier. Keywords. Hedge algebras, fuzzy classification system, feature selection, high-dimensional dataset. 1. INTRODUCTION The fuzzy rule based classification system (FRBCS) design problem is one of the concerned study trends in the data mining field and has achieved many successful results. The advantage of this model is that the end-users can use the high interpretability fuzzy rule based knowledge extracted automatically from numerical data as their knowledge. In the fuzzy set theory approaches for designing FRBCS [1,2,12,13],