[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5370/JEET.2014.9.6.2098

Polynomial Fuzzy Radial Basis Function Neural Network Classifiers Realized with the Aid of Boundary Area Decision

Roh, Seok-Beom (Dept. of Electronic and Information Engineering, Wonkwang University)
Oh, Sung-Kwun (Dept. of Electrical Engineering, The University of Suwon)

Publication Information

Journal of Electrical Engineering and Technology / v.9, no.6, 2014 , pp. 2098-2106 More about this Journal

Abstract

In the area of clustering, there are numerous approaches to construct clusters in the input space. For regression problem, when forming clusters being a part of the overall model, the relationships between the input space and the output space are essential and have to be taken into consideration. Conditional Fuzzy C-Means (c-FCM) clustering offers an opportunity to analyze the structure in the input space with the mechanism of supervision implied by the distribution of data present in the output space. However, like other clustering methods, c-FCM focuses on the distribution of the data. In this paper, we introduce a new method, which by making use of the ambiguity index focuses on the boundaries of the clusters whose determination is essential to the quality of the ensuing classification procedures. The introduced design is illustrated with the aid of numeric examples that provide a detailed insight into the performance of the fuzzy classifiers and quantify several essentials design aspects.

Keywords

Polynomial fuzzy radial basis function neural network; Conditional fuzzy clustering; Pattern classification; Imbalanced data;

Citations & Related Records

Times Cited By KSCI : 1 (Citation Analysis)

Reference
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1573-7497	(2019) Applied Intelligence Design of fuzzy radial basis function neural network classifier based on information data preprocessing for recycling black plastic wastes: comparative studies of ATR FT-IR and Raman spectroscopy / (1573-7497)