Paper Info
Title
Rough sets and vague concept approximation: from sample approximation to adaptive learning
Abstract
We present a rough set approach to vague concept approximation. Approximation spaces used for concept approximation have been initially defined on samples of objects (decision tables) representing partial information about concepts. Such approximation spaces defined on samples are next inductively extended on the whole object universe. This makes it possible to define the concept approximation on extensions of samples. We discuss the role of inductive extensions of approximation spaces in searching for concept approximation. However, searching for relevant inductive extensions of approximation spaces defined on samples is infeasible for compound concepts. We outline an approach making this searching feasible by using a concept ontology specified by domain knowledge and its approximation. We also extend this approach to a framework for adaptive approximation of vague concepts by agents interacting with environments. This paper realizes a step toward approximate reasoning in multiagent systems (MAS), intelligent systems, and complex dynamic systems (CAS).
Year
DOI
Venue
2006
10.1007/11847465_3
T. Rough Sets
Keywords
Field
DocType
higher order,rough set,domain knowledge,decision table,reinforcement learning,intelligent systems,rough sets,adaptive learning
Discrete mathematics,Vagueness,Decision table,Intelligent decision support system,Domain knowledge,Computer science,Algorithm,Rough set,Theoretical computer science,Adaptive learning,Reinforcement learning
Journal
Volume
ISSN
ISBN
4100
0302-9743
3-540-39382-X
Citations 
PageRank 
References 
30
1.02
33
Authors
3
Name
Order
Citations
PageRank
Jan G. Bazan129122.71
Andrzej Skowron25062421.31
Roman W. Swiniarski372243.70