Abstract
The article describes a new rough-fuzzy model for pattern classification. Here, class-dependent granules are formulated in fuzzy environment that preserve better class discriminatory information. Neighborhood rough sets (NRS) are used in the selection of a subset of granulated features that explore the local/contextual information from neighbor granules. The model thus explores mutually the advantages of class-dependent fuzzy granulation and NRS that is useful in pattern classification with overlapping classes. The superiority of the proposed model to other similar methods is demonstrated with both completely and partially labeled data sets using various performance measures. The proposed model learns well even with a lower percentage of training set that makes the system fast.
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Pal, S.K., Meher, S.K., Dutta, S. (2010). Pattern Classification Using Class-Dependent Rough-Fuzzy Granular Space. In: Yu, J., Greco, S., Lingras, P., Wang, G., Skowron, A. (eds) Rough Set and Knowledge Technology. RSKT 2010. Lecture Notes in Computer Science(), vol 6401. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16248-0_13
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DOI: https://doi.org/10.1007/978-3-642-16248-0_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16247-3
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