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

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 [12]. 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 [12] by utilizing the feature selection algorithm proposed in [18]. 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.