In the field of soft computing the need of more flexible, fast and robust approaches is evident. The main idea behind this paper is the construction of sets of fuzzy rules with a hierarchical structure derived from data. The appropriate set is then applied on demand. Instead of rule pruning or rule refinement, we propose a way that sorts the total of rules of a well-trained fuzzy neural network in order of significance and creates prioritized rule sets using sensitivity analysis. A confidence measure indicates the appropriate set to be utilized at any given time. Experiments in benchmark classification tasks prove that this method does not only reduce computational cost, but it also maintains performance at the same levels, offering fast processing during real-time operations
['Minas Pertselakis', 'Andreas Stafylopatis']
Dynamic Rule Structuring based on Rule-Activation Sensitivity Analysis