Granular classifiers and their design through refinement of information granules

In this study, we focus on the design and refinements of granular pattern classifiers, namely classifiers, which deal with a collection of information granules formed in a certain feature space. The development of this category of classifiers is realized as a two-phase design process. First, information granules occupying some regions of the feature space are formed through invoking mechanisms of clustering or fuzzy clustering. As a result, regions in the feature space are built, which are densely occupied by the patterns predominantly belonging to the same class. We offer a detailed way of assessing the character and quality of information granules and their information (classification-oriented) content. The resulting description is utilized in the realization of the classification mechanism being considered at the second phase of the design of the granular classifier. The mapping from the collection of information granules to class assignment (classification) involves matching of a pattern to be classified to individual information granules and aggregating them by considering the information content of the corresponding granules. In the study, a number of descriptors capturing information content and aggregation functions are analyzed. To improve the performance of the granular classifier, a refinement of information granules is carried out, in which highly heterogeneous information granules (viz. those containing patterns belonging to various classes) are refined (split, specialized), and their refined versions are afterwards used in the buildup of the classifier. A series of experiments involving both synthetic data as well as those publicly available is reported and analyzed, illustrating the main advantages of granular classifiers and their design procedure.