A design of granular fuzzy classifier
A new design methodology of granular fuzzy classifiers based on information granules is proposed. The information granules are constructed in terms of a geometrical distribution of patterns. The elements in an information granule are heterogeneous with respect to their class distributions. We improve the class homogeneity of the originally constructed information granules by using Particle Swarm Optimization. We consider the center points of the information granules as prototypes of the prototype based classifiers.
granular fuzzy classifier information granules pattern classifier weighting scheme