On Generalisation in Michigan-Style Fuzzy Classifier Systems

The ability of a rule-based system to represent generalisations is of great importance. Generalised rules allow more compact rule bases, scalability to higher dimensional spaces, faster inference and better linguistic interpretability. The issue of rule generalisation, and the interplay between general and specific rules in the same evolving population, has received a great deal of attention in the discrete-valued classifier system research community. The same issue does not appear to have received a similar level of attention in the case of fuzzy classifier systems. While it is true that generalised rule representations have been incorporated in a number of reported fuzzy classifier systems, often as an aside to other significant issues, this important feature has not yet been concentrated on in detail. The intention of this contribution is to raise awareness of the issue of generalisation in the fuzzy classifier system so that the issue may be brought under closer scrutiny. Early experimental results using a test-bed specifically designed to test the ability of the fuzzy classifier system to learn an optimal collection of co-existing general and specific fuzzy classifiers are described and discussed.