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Probability measures for prediction in multi-table infomation systems

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2005) 3776 LNCS 738-743
DOI: 10.1007/11590316_119
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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. © Springer-Verlag Berlin Heidelberg 2005.

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APA

Milton, R. S., Maheswari, V. U., & Siromoney, A. (2005). Probability measures for prediction in multi-table infomation systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3776 LNCS, pp. 738–743). https://doi.org/10.1007/11590316_119

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