Data uncertainty is common in real-world applications due to various causes, including imprecise measurement, network latency, out-dated sources and sampling errors. As a result there is a need for tools and techniques for mining and managing uncertain data. In this paper proposes a Rough Set method for handling data uncertainty. Rough set is a mathematical theory for dealing with uncertainty. Uncertainty implies inconsistencies, which are taken into account, so that the produced are categorized into certain and possible with the help of rough set theory Experimental results show that proposed model exhibits reasonable accuracy performance in classification on uncertain data. © 2012 Springer-Verlag GmbH Berlin Heidelberg.
CITATION STYLE
Suresh, G. V., Venkateswara Reddy, E., & Srinivasa Reddy, E. (2012). Uncertain data classification using rough set theory. In Advances in Intelligent and Soft Computing (Vol. 132 AISC, pp. 869–877). Springer Verlag. https://doi.org/10.1007/978-3-642-27443-5_100
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