The use of fuzzy quantifiers to modify the fuzzy linguistic terms in fuzzy models helps build fuzzy systems in a more natural way, by capturing finer pieces of information embedded in the training data. This paper presents a practical approach for the acquisition of fuzzy production rules with quantifiers, based on a class-dependent simultaneous rule learning strategy where each class is associated with a subset of descriptive rules. It is implemented by particle swam optimisation. The performance of the learned fuzzy rules with and without fuzzy quantifiers is evaluated on various UCI benchmark data sets, in comparison to popular alternative rule based learning classifiers. Experimental results demonstrate that rule bases generated by the proposed approach indeed boost classification performance as compared to those involving no fuzzy quantifiers, with at least competitive performance to the alternative learning classifiers.
|Title of host publication||Proceedings of the 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), (Istanbul, 2-5 August 2015)|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||7|
|Publication status||Published - 30 Nov 2015|
Chen, T., Shen, Q., Su, P., & Shang, C. (2015). Induction of Quantified Fuzzy Rules with Particle Swarm Optimisation. In Proceedings of the 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), (Istanbul, 2-5 August 2015) Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FUZZ-IEEE.2015.7337883