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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References
Bargiela, A., Pedrycz, W.: Granular Computing: An Introduction. Kluwer Academic Publishers, Boston (2003)
Zadeh, L.A.: Fuzzy sets and information granularity. In: Gupta, M., Ragade, R., Yager, R. (eds.) Advances in Fuzzy Set Theory and Applications, pp. 3–18. North-Holland Publishing Co., Amsterdam (1979)
Zadeh, L.A.: Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems 90, 111–127 (1997)
Pawlak, Z.: Rough sets. International Journal of Computer and Information Science 11, 341–356 (1982)
Pal, S.K., Skowron, A. (eds.): Rough-Fuzzy Hybridization: A New Trend in Decision Making. Springer, Singapore (1999)
Pal, S.K., Mitra, S.: Multilayer perceptron, fuzzy sets, and classification. IEEE Trans. Neural Networks 3, 683–697 (1992)
Lin, T.Y.: Granulation and nearest neighborhoods: rough set approach. In: Pedrycz, W. (ed.) Granular computing: an emerging paradigm, pp. 125–142. Physica-Verlag, Heidelberg (2001)
Hu, Q., Yu, D., Liu, J., Wu, C.: Neighborhood rough set based heterogeneous feature subset selection. Inf. Sciences 178, 3577–3594 (2008)
Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)
Cohen, J.: A coefficient of aggrement for nominal scale. Education and psychological measurement 20, 37–46 (1960)
Pal, S.K., Ghosh, A., Uma Shankar, B.: Segmentation of remotely sensed images with fuzzy thresholding, and quatitative evaluation. Int. J. of Remote Sensing 21, 2269–2300 (2000)
Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Machine Intell. 1, 224–227 (1979)
Pal, S.K., Majumder, D.D.: Fuzzy sets and decision making approaches in vowel and speaker recognition. IEEE Trans. Syst., Man Cybern. 7, 625–629 (1977)
Elena database, ftp://ftp.dice.ucl.ac.be/pub/neural-nets/ELENA/databases/REAL/
NRSA, IRS data users hand book, Technical report, Document No. IRS/NRSA/NDC/HB-02/89 (1989)
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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
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