Proceedings Article10.1109/FUZZY.1996.552272
Why rough sets
Zdzisław Pawlak
- 08 Sep 1996
- Vol. 2, pp 738-743
TL;DR: Rough set theory overlaps with many other theories, especially with fuzzy set theory, evidence theory and Boolean reasoning methods, but can be viewed in its own rights, as an independent, complementary, and not competing discipline.
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Abstract: The problem of imperfect knowledge has been tackled for a long time by philosophers, logicians and mathematicians. Recently it became also a crucial issue for computer scientists, particularly in the area of artificial intelligence. There are many approaches to the problem of how to understand and manipulate the imperfect knowledge. The most successful one is, no doubt, fuzzy set theory proposed by Zadeh. Rough set theory is another attempt to this problem. The theory has attracted attention of many researchers and practitioners all over the world, who contributed essentially to its development and applications. Rough set theory overlaps with many other theories, especially with fuzzy set theory, evidence theory and Boolean reasoning methods-nevertheless it can be viewed in its own rights, as an independent, complementary, and not competing discipline.
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Citations
A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis
TL;DR: Experimental results demonstrate the proposed rough set based supporting vector machine classifier (RS_SVM) can not only achieve very high classification accuracy but also detect a combination of five informative features, which can give an important clue to the physicians for breast diagnosis.
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References
Rough sets
TL;DR: This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.
7.2K
•Journal Article
On rough sets
TL;DR: The presented approach may be considered as an alternative to fuzzy sets theory and tolerance theory and some applications are outlined.
Variable precision rough set model
TL;DR: A generalized model of rough sets called variable precision model (VP-model), aimed at modelling classification problems involving uncertain or imprecise information, is presented and the main concepts are introduced formally and illustrated with simple examples.
2K
Putting Rough Sets and Fuzzy Sets Together
Didier Dubois,Henri Prade +1 more
- 01 Jan 1992
TL;DR: It is argued that fuzzy sets and rough sets aim to different purposes and that it is more natural to try to combine the two models of uncertainty (vagueness for fuzzy set and coarseness for rough sets) in order to get a more accurate account of imperfect information.
723
•Book
"Rough Sets, Fuzzy Sets and Knowledge Discovery"
Wojciech Ziarko,C. J. Van Rijsbergen +1 more
- 09 Sep 1994
TL;DR: An Overview of Knowledge Discovery in Databases: Recent Progress and Challenges.
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