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.
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Yao, J., Yao, Y., Zhao, Y. Foundations of Classification. In: Young Lin, T., Ohsuga, S., Liau, CJ., Hu, X. (eds) Foundations and Novel Approaches in Data Mining. Studies in Computational Intelligence, vol 9. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539827_5
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DOI: https://doi.org/10.1007/11539827_5
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28315-7
Online ISBN: 978-3-540-31229-1
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