A fuzzy neural network approach to classification based on proximity characteristics of patterns
Konstantinos Blekas, Aristidis C. Likas, Andreas Stafylopatis · 2002
A neural network classifier is presented, which is based on geometrical fuzzy sets. Starting from the construction of the Voronoi diagram of the training patterns, an aggregation of Voronoi regions is performed leading to the identification of larger regions belonging exclusively to one of the pattern classes. The resulting scheme is a constructive algorithm that defines fuzzy clusters of patterns. Based on observations concerning the grade of membership of the training patterns to the created regions, decision probabilities are computed through which the final classification is performed. Experimental results concerning several classification problems indicate that the proposed method achieves high classification rates and compares favorably with other well-known approaches.