Abstract
It is estimated that every 20 months or so the amount of information in the world doubles. In the same way, tools that mine knowledge from data must develop to combat this growth. Fuzzy-rough set theory provides a framework for developing such applications in a way that combines the best properties of fuzzy sets and rough sets, in order to handle uncertainty. In this tutorial we will cover the mathematical groundwork required for an understanding of the data mining methods, before looking at some of the key developments in the area, including feature selection and classifier learning.
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Jensen, R. (2011). Fuzzy-Rough Data Mining. In: Kuznetsov, S.O., Ślęzak, D., Hepting, D.H., Mirkin, B.G. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2011. Lecture Notes in Computer Science(), vol 6743. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21881-1_7
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DOI: https://doi.org/10.1007/978-3-642-21881-1_7
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