On the correctness of rough-set based approximate reasoning

Authors:
Patrick Doherty;Andrzej Szałas
Affiliations:
Dept. of Computer and Information Science, Linköping University, Linköping, Sweden;Institute of Informatics, Warsaw University, Warsaw, Poland
Venue:
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Year:
2010

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Abstract

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?