Ordering fuzzy sets for class separation purposes

Ranking fuzzy subsets is an important event in dealing with fuzzy decision problems in many areas, such as management sciences, engineering, and even social sciences. This issue has been of concern for many researchers over the years. Some twenty eight methods have been proposed in the publications for ranking fuzzy subsets. It is the purpose of this paper to evaluate and analyze the performance of available methods of ranking fuzzy subsets on a set of selected examples that cover possible situations one might encounter as defining fuzzy subsets at each node of a neural network. Through this analysis, suggestions as to which methods have better performance for utilization in neural network architectures, as well as criteria for choosing an appropriate method for ranking in corresponding fuzzy trees are made