Reduction of Fuzzy Rule Bases Driven by the Coverage of Training Data

DOI

10.2991/ifsa-eusflat-15.2015.67How to use a DOI?

Keywords

Fuzzy association rules, fuzzy rule base, reduction, coverage.

Abstract

We present a technique for size reduction of a base of fuzzy association rules which is created using an automated approach and which is intended for inference. Our approach is based on controlling the coverage of training data by the rule base and removing only such rules that do not increase that coverage. Experiments show that such reduction is very effective while affecting the outputs of inference only very slightly.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Michal Burda
AU  - Martin Stepnicka
PY  - 2015/06
DA  - 2015/06
TI  - Reduction of Fuzzy Rule Bases Driven by the Coverage of Training Data
BT  - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology
PB  - Atlantis Press
SP  - 463
EP  - 470
SN  - 1951-6851
UR  - https://doi.org/10.2991/ifsa-eusflat-15.2015.67
DO  - 10.2991/ifsa-eusflat-15.2015.67
ID  - Burda2015/06
ER  -