Abstract
Rough Set Theory is a mathematical tool to deal with vagueness and uncertainty. Rough Set Theory uses a single information table. Relational Learning is the learning from multiple relations or tables. This paper studies the use of Rough Set Theory and Variable Precision Rough Sets in a multi-table information system (MTIS). The notion of approximation regions in the MTIS is defined in terms of those of the individual tables. This is used in classifying an example in the MTIS, based on the elementary sets in the individual tables to which the example belongs. Results of classification experiments in predictive toxicology based on this approach are presented.
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Milton, R.S., Maheswari, V.U., Siromoney, A. (2005). Studies on Rough Sets in Multiple Tables. In: Ślęzak, D., Wang, G., Szczuka, M., Düntsch, I., Yao, Y. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2005. Lecture Notes in Computer Science(), vol 3641. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11548669_28
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DOI: https://doi.org/10.1007/11548669_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28653-0
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