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Uncertain Data Classification Using Rough Set Theory

  • G. Vijay Suresh
  • E. Venkateswara Reddy
  • E. Srinivasa Reddy
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 132)

Abstract

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.

Keywords

Uncertain Data Artificial Intelligence Approach Existential Uncertainty Knowledge Engineer Review Conceptual Schema Design 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • G. Vijay Suresh
    • 1
  • E. Venkateswara Reddy
    • 1
  • E. Srinivasa Reddy
    • 1
  1. 1.University College of Engineering & Technology, ANUGunturIndia

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