Constructing a new fuzzy classifier based on hierarchical fuzzy entropy

Abstract

In an earlier work, Lee et al. (Lee et al., 2001) presented a simple and fast fuzzy classifier that employed fuzzy entropy to evaluate pattern distribution information in a pattern space. In this paper, we extend his work to propose a new fuzzy classifier based on hierarchical fuzzy entropy (FC‐HFE). We retained the main parts of the original structure and modified some methods (e.g., methods for deciding the number of intervals in each dimension and for assigning class labels). In addition, the hierarchical fuzzy entropy is proposed for partitioning the decision region. The proposed FC‐HFE improves classification accuracy and overcomes some of the drawbacks in the Lee et al method (Lee et al., 2001). The simulation results show that the classification rate of the proposed FC‐HFE is better than earlier methods.