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Vaguely Quantified Rough Sets

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Abstract
The hybridization of rough sets and fuzzy sets has focused on creating an end product that extends both contributing computing paradigms in a conservative way. As a result, the hybrid theory inherits their respective strengths, but also exhibits some weaknesses. In particular, although they allow for gradual membership, fuzzy rough sets are still abrupt in a sense that adding or omitting a single element may drastically alter the outcome of the approximations. In this paper, we revisit the hybridization process by introducing vague quantifiers like "some" or "most" into the definition of upper and lower approximation. The resulting vaguely quantified rough set (VQRS) model is closely related to Ziarko's variable precision rough set (VPRS) model.

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Citation

Please use this url to cite or link to this publication:

MLA
Cornelis, Chris, et al. “Vaguely Quantified Rough Sets.” Lecture Notes in Artificial Intelligence, vol. 4482, Springer, 2007, pp. 87–94.
APA
Cornelis, C., De Cock, M., & RADZIKOWSKA, A. (2007). Vaguely Quantified Rough Sets. Lecture Notes in Artificial Intelligence, 4482, 87–94. Berlin: Springer.
Chicago author-date
Cornelis, Chris, Martine De Cock, and A RADZIKOWSKA. 2007. “Vaguely Quantified Rough Sets.” In Lecture Notes in Artificial Intelligence, 4482:87–94. Berlin: Springer.
Chicago author-date (all authors)
Cornelis, Chris, Martine De Cock, and A RADZIKOWSKA. 2007. “Vaguely Quantified Rough Sets.” In Lecture Notes in Artificial Intelligence, 4482:87–94. Berlin: Springer.
Vancouver
1.
Cornelis C, De Cock M, RADZIKOWSKA A. Vaguely Quantified Rough Sets. In: Lecture Notes in Artificial Intelligence. Berlin: Springer; 2007. p. 87–94.
IEEE
[1]
C. Cornelis, M. De Cock, and A. RADZIKOWSKA, “Vaguely Quantified Rough Sets,” in Lecture Notes in Artificial Intelligence, Toronto, Canada, 2007, vol. 4482, pp. 87–94.
@inproceedings{391308,
  abstract     = {{The hybridization of rough sets and fuzzy sets has focused on creating an end product that extends both contributing computing paradigms in a conservative way. As a result, the hybrid theory inherits their respective strengths, but also exhibits some weaknesses. In particular, although they allow for gradual membership, fuzzy rough sets are still abrupt in a sense that adding or omitting a single element may drastically alter the outcome of the approximations. In this paper, we revisit the hybridization process by introducing vague quantifiers like "some" or "most" into the definition of upper and lower approximation. The resulting vaguely quantified rough set (VQRS) model is closely related to Ziarko's variable precision rough set (VPRS) model.}},
  author       = {{Cornelis, Chris and De Cock, Martine and RADZIKOWSKA, A}},
  booktitle    = {{Lecture Notes in Artificial Intelligence}},
  isbn         = {{978-3-540-72529-9}},
  issn         = {{0302-9743}},
  language     = {{eng}},
  location     = {{Toronto, Canada}},
  pages        = {{87--94}},
  publisher    = {{Springer}},
  title        = {{Vaguely Quantified Rough Sets}},
  volume       = {{4482}},
  year         = {{2007}},
}

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