Foundations of Classification

Abstract

Classification is one of the main tasks in machine learning, data mining, and pattern recognition. A granular computing model is suggested for learning two basic issues of concept formation and concept relationship identification. A classification problem can be considered as a search for suitable granules organized under a partial order. The structures of search space, solutions to a consistent classification problem, and the structures of solution space are discussed. A classification rule induction method is proposed. Instead of searching for a suitable partition, we concentrate on the search for a suitable covering of the given universe. This method is more generalthan partition-based methods. For the design of covering granule selection heuristics, several measures on granules are suggested.

Keywords

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.