LiU Electronic Press
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Author:
Doherty, Patrick (Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab) (Linköping University, The Institute of Technology)
Szalas, Andrzej (Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab) (Linköping University, The Institute of Technology)
Title:
On the Correctness of Rough-Set Based Approximate Reasoning
Department:
Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab
Linköping University, The Institute of Technology
Publication type:
Conference paper (Refereed)
Language:
English
In:
Proceedings of the 7th International Conference on Rough Sets and Current Trends in Computing (RSCTC)
Editor:
M. Szczuka, M. Kryszkiewicz, S. Ramanna, R. Jensen, Q. Hu
Publisher: Springer
Series:
Lecture Notes in Computer Science, ISSN 0302-9743; 6086
Volume:
6086
Pages:
327-336
Year of publ.:
2010
URI:
urn:nbn:se:liu:diva-59726
Permanent link:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-59726
ISBN:
978-3-642-13528-6
ISI:
000281605400035
Subject category:
Engineering and Technology
SVEP category:
TECHNOLOGY
Abstract(en) :

There is a natural generalization of an indiscernibility relation used in rough set theory, where rather than partitioning the universe of discourse into indiscernibility classes, one can consider a covering of the universe by similarity-based neighborhoods with lower and upper approximations of relations defined via the neighborhoods. When taking this step, there is a need to tune approximate reasoning to the desired accuracy. We provide a framework for analyzing self-adaptive knowledge structures. We focus on studying the interaction between inputs and output concepts in approximate reasoning. The problems we address are: -given similarity relations modeling approximate concepts, what are similarity relations for the output concepts that guarantee correctness of reasoning? -assuming that output similarity relations lead to concepts which are not accurate enough, how can one tune input similarities?

Available from:
2010-09-24
Created:
2010-09-24
Last updated:
2012-02-13
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