透過您的圖書館登入
IP:3.141.202.187
  • 期刊

Knowledge Reducts to Incomplete Information System under the Similarity Relation

並列摘要


Certain rules and possible rules exist in incomplete information system, So membership function and generalized decision function under the similarity relation are proposed and some properties of them are proved. Based on the concepts, several types of knowledge reducts to object and system are defined under similarity relation, and mutual relationship among them is established. Several kinds of decision rules are defined according to the new definition of knowledge reducts. An example shows how to generate optimal certain rule and optimal generalized rule by using discernibility function, and the result shows that different knowledge reducts lead to different decision rules. The research on types of knowledge reducts is the theory foundation of knowledge acquisition algorithms to incomplete information system.

參考文獻


Grzymala-Busse, J.,Zou, X.(1998).Classification Strategies Using Certain and Possible Rules.LNAI 1424, RSCTC'98.(LNAI 1424, RSCTC'98).
Stefanowski, J.(1998).On rough set based approaches to induction of decision rules.Rough Set in Knowledge Discovery.(Rough Set in Knowledge Discovery).:
Slezak, D.(1998).Searching for dynamic reducts in inconsistent decision tables.Proc of IPMU'98.(Proc of IPMU'98).:
Nguyen, HS,Slezak, D.(1999).Approximate reducts and association rules correspondence and complexity results.Proc of RSFDGRC'99.(Proc of RSFDGRC'99).:
Kryszkiewicz, M.(1999).Rules in incomplete information systems.Information Sciences.113,271-292.

延伸閱讀