Reduction of Fuzzy Rule Bases Driven by the Coverage of Training Data
Michal Burda, Martin Štěpnička · Advances in intelligent systems research/Advances in Intelligent Systems Research · 2015
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.