Incomplete Information Tables and Rough Classification

@article{Stefanowski2001IncompleteIT,
  title={Incomplete Information Tables and Rough Classification},
  author={Jerzy Stefanowski and Alexis Tsouki{\'a}s},
  journal={Computational Intelligence},
  year={2001},
  volume={17}
}
The rough set theory, based on the original definition of the indiscernibility relation, is not useful for analysing incomplete information tables where some values of attributes are unknown. In this paper we distinguish two different semantics for incomplete information: the “missing value” semantics and the “absent value” semantics. The already known approaches, e.g. based on the tolerance relations, deal with the missing value case. We introduce two generalisations of the rough sets theory… 

An extension model of rough set in incomplete information system

  • Yun WuQingshun Guo
  • Computer Science
    2010 2nd International Conference on Future Computer and Communication
  • 2010
A new extension model of rough set is proposed based on the non-symmetric similarity relation and the limited tolerance relation and it is proposed that this relation is superior to the classical rough set model.

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