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On the Correctness of Rough-Set Based Approximate Reasoning
2010 (English)In: Proceedings of the 7th International Conference on Rough Sets and Current Trends in Computing (RSCTC) / [ed] M. Szczuka, M. Kryszkiewicz, S. Ramanna, R. Jensen, Q. Hu, Springer, 2010, Vol. 6086, 327-336Conference paper (Refereed)
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?

Lecture Notes in Computer Science, ISSN 0302-9743 ; 6086
National Category
Engineering and Technology
URN: urn:nbn:se:liu:diva-59726DOI: 10.1007/978-3-642-13529-3_35ISI: 000281605400035ISBN: 978-3-642-13528-6OAI: diva2:353173
Available from2010-09-24 Created:2010-09-24 Last updated:2012-02-13

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Doherty, PatrickSzalas, Andrzej
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KPLAB - Knowledge Processing LabThe Institute of Technology
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