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An Approach to Pattern Recognition Based on Hierarchical Granular Computing

Nguyen S.H., Nguyen T.T., Szczuka M., Nguyen H.S.

Fundamenta Informaticae

2013

Vol. 127, nr 1-4

369--384

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.

Learning Sunspot Classification

Nguyen T.T., Willis C.P., Paddon D.J., Nguyen S.H., Nguyen H.S.

Fundamenta Informaticae

2006

Vol. 72, nr 1-3

295-309

Sunspots are the subject of interest to many astronomers and solar physicists. Sunspot observation, analysis and classification form an important part of furthering the knowledge about the Sun. Sunspot classification is a manual and very labor intensive process that could be automated if successfully learned by a machine. This paper presents machine learning approaches to the problem of sunspot classification. The classification scheme attempted was the seven-class Modified Zurich scheme [18]. The data was obtained by processing NASA SOHO/MDI satellite images to extract individual sunspots and their attributes. A series of experiments were performed on the training dataset with an aim of learning sunspot classification and improving prediction accuracy. The experiments involved using decision trees, rough sets, hierarchical clustering and layered learning methods. Sunspots were characterized by their visual properties like size, shape, positions, and were manually classified by comparing extracted sunspots with corresponding active region maps (ARMaps) from the Mees Observatory at the Institute for Astronomy, University of Hawaii.