Using degree of consensus in two-level fuzzy pattern recognition

Abstract

A two-level fuzzy pattern recognition scheme is proposed which implements different classification rules at the second level in dependence on the degree of consensus between first-level decision makers. The first-level classification decisions are treated in a fuzzy manner, i.e. each one is supposed to provide class memberships to all classes for each object submitted to classification. The main aim is to increase the classification accuracy of automatic classifiers. The presumption behind the idea is that different rules may appear suitable for the cases of low, medium, or high consensus between first-level decisions and neither of these rules works sufficiently well in all cases. Some implementation details are specified in the text but the real application of the scheme is not confined to them only. A set of experimental results with real data from aviation medicine is presented which show the anticipated increase of the classification accuracy.