Abstract
Pawlak rough set theory is a powerful mathematic tool to deal with imprecise, uncertainty and incomplete dataset. In this paper, we study the fuzzy rough set attribute reduction (fuzzy RSAR) in fuzzy information systems. Firstly, we present the formal definition of a kind new rough set form-the composed fuzzy rough set. The second, some properties of extension forms of Pawlak rough set are also discussed. Lastly, we illustrate the fuzzy RSAR based on composed fuzzy rough set, and a simple example is given to show this approach can retain less attributes and entailing higher classification accuracy than the crisp RST-based reduction method.
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© 2007 Springer-Verlag Berlin Heidelberg
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Qiu, W., Hu, Z. (2007). Composed Fuzzy Rough Set and Its Applications in Fuzzy RSAR. In: Xu, M., Zhan, Y., Cao, J., Liu, Y. (eds) Advanced Parallel Processing Technologies. APPT 2007. Lecture Notes in Computer Science, vol 4847. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76837-1_81
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DOI: https://doi.org/10.1007/978-3-540-76837-1_81
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76836-4
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