Abstract
•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.
In this paper, we propose a new design methodology of granular fuzzy classifiers based on a concept of information granularity and information granules. The classifier uses the mechanism of information granulation with the aid of which the entire input space is split into a collection of subspaces. When designing the proposed fuzzy classifier, these information granules are constructed in a way they are made reflective of the geometry of patterns belonging to individual classes. Although the elements involved in the generated information granules (clusters) seem to be homogeneous with respect to the distribution of patterns in the input (feature) space, they still could exhibit a significant level of heterogeneity when it comes to the class distribution within the individual clusters. To build an efficient classifier, we improve the class homogeneity of the originally constructed information granules (by adjusting the prototypes of the clusters) and use a weighting scheme as an aggregation mechanism.