Pattern classification using class-dependent rough-fuzzy granular space

Authors:
Sankar K. Pal;Saroj K. Meher;Soumitra Dutta
Affiliations:
Center for Soft Computing Research, Indian Statistical Institute, Kolkata, India;Center for Soft Computing Research, Indian Statistical Institute, Kolkata, India;INSEAD, France
Venue:
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Year:
2010

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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.