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- W1529546211 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." @default.
- W1529546211 created "2016-06-24" @default.
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- W1529546211 date "2010-01-01" @default.
- W1529546211 modified "2023-10-07" @default.
- W1529546211 title "Pattern Classification Using Class-Dependent Rough-Fuzzy Granular Space" @default.
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- W1529546211 doi "https://doi.org/10.1007/978-3-642-16248-0_13" @default.
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