Rough Sets

Rough Sets

Zdzislaw Pawlak (Polish Academy of Sciences and Warsaw School of Information Technology, Poland), Lech Polkowski (Polish-Japanese Institute of Information Technology and University of Warmia and Mazury, Poland) and Andrzej Skowron (Warsaw University, Poland)
Copyright: © 2005 |Pages: 6
DOI: 10.4018/978-1-59140-560-3.ch095
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Rough set theory is a new mathematical approach to imperfect knowledge. The problem of imperfect knowledge, tackled for a long time by philosophers, logicians, and mathematicians, has become 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 imperfect knowledge. The most successful one is, no doubt, fuzzy set theory proposed by Zadeh (1965). Rough set theory (Pawlak, 1982) presents still another attempt at this problem. This theory has attracted the attention of many researchers and practitioners all over the world, who contributed essentially to its development and applications. The rough set approach seems to be of fundamental importance to AI and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, inductive reasoning, and pattern recognition.

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