Abstract
This paper presents system which tries to combine the advantages of rough sets methods and fuzzy sets methods to get better classification. The fuzzy sets theory supports approximate reasoning and the rough sets theory is responsible for data analyzing and process of automatic fuzzy rules generation. The system was designed as a typical knowledge based system consisting of four main parts: rule extractor, knowledge base, inference engine, user interface and occurs to be useful tool in various decision problems and fuzzy control.
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© 2004 Springer-Verlag Berlin Heidelberg
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Drwal, G., Sikora, M. (2004). Fuzzy Decision Support System with Rough Set Based Rules Generation Method. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds) Rough Sets and Current Trends in Computing. RSCTC 2004. Lecture Notes in Computer Science(), vol 3066. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25929-9_92
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DOI: https://doi.org/10.1007/978-3-540-25929-9_92
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22117-3
Online ISBN: 978-3-540-25929-9
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