Abstract
This article provides an overview of recent literature on some tasks of pattern recognition using rough sets and its hybridization with other soft computing paradigms. Rough set theory is an established tool for dealing with imprecision, noise, and uncertainty in data. In this article we will focus on two recent applications using rough sets; viz., feature selection in high dimensional gene expression data, and collaborative clustering. The experimental results demonstrate that the incorporation of rough set improves the performance of the system.
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Mitra, S., Banka, H. (2007). Application of Rough Sets in Pattern Recognition. In: Peters, J.F., Skowron, A., Marek, V.W., Orłowska, E., Słowiński, R., Ziarko, W. (eds) Transactions on Rough Sets VII. Lecture Notes in Computer Science, vol 4400. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71663-1_10
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DOI: https://doi.org/10.1007/978-3-540-71663-1_10
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