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 presents a new approach to the extension of Rough Set Theory to multiple relations or tables. The utility of this approach is shown in classification experiments in predictive toxicology.
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Milton, R.S., Maheswari, V.U., Siromoney, A. (2005). Probability Measures for Prediction in Multi-table Infomation Systems. In: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds) Pattern Recognition and Machine Intelligence. PReMI 2005. Lecture Notes in Computer Science, vol 3776. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11590316_119
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DOI: https://doi.org/10.1007/11590316_119
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30506-4
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