Abstract
This paper presents the RClass system, which was designed as a tool for data validation and the classification of uncertain information. This system uses rough set theory based methods to allow handling uncertain information. Some of proposed classification algorithms also employ fuzzy set theory in order to increase a classification quality. The knowledge base of the RClass system is expressed as a deterministic or non-deterministic decision table with quantitative or qualitative values of attributes, and can be imported from standard databases or text files.
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© 2001 Springer-Verlag Berlin Heidelberg
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Drwal, G. (2001). Rough, and Fuzzy-Rough Classification Methods Implemented in RClass System. In: Ziarko, W., Yao, Y. (eds) Rough Sets and Current Trends in Computing. RSCTC 2000. Lecture Notes in Computer Science(), vol 2005. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45554-X_18
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DOI: https://doi.org/10.1007/3-540-45554-X_18
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