Open AccessPolynomial Fuzzy Radial Basis Function Neural Network Classifiers Realized with the Aid of Boundary Area Decision
Author(s) -
Seok-Beom Roh,
SungKwun Oh
Publication year - 2014
Publication title -
journal of electrical engineering and technology/journal of electrical engineering and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.226
H-Index - 27
eISSN - 2093-7423
pISSN - 1975-0102
DOI - 10.5370/jeet.2014.9.6.2098
Subject(s) - cluster analysis , data mining , artificial intelligence , basis (linear algebra) , fuzzy logic , computer science , space (punctuation) , ambiguity , artificial neural network , mathematics , machine learning , fuzzy clustering , radial basis function , boundary (topology) , geometry , programming language , operating system , mathematical analysis
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.