An Approach to Pattern Recognition Based on Hierarchical Granular Computing

This paper summarizes the some of the recent developments in the area of application of rough sets and granular computing in hierarchical learning. We present the general framework of rough set based hierarchical learning. In particular, we investigate several strategies of choosing the appropriate learning algorithms for first level concepts as well as the learning methods for the intermediate concepts. We also propose some techniques for embedding the domain knowledge into the granular, layered learning process in order to improve the quality of hierarchical classifiers. This idea, which has been envisioned and developed by professor Andrzej Skowron over the last 10 years, shows to be very efficient in many practical applications. Throughout the article, we illustrate the proposed methodology with three case studies in the area of pattern recognition. The studies demonstrate the viability of this approach for such problems as: sunspot classification, hand-written digit recognition, and car identification.