Application of cost-sensitive fuzzy classifiers to image understanding problems
Gerald Schaefer, Tomoharu Nakashima · 2009
Image understanding applications often involve a pattern classification stage. In this paper we show how a fuzzy rule-based classifier, extended to incorporate a cost function, can be successfully used in various imaging applications. The antecedent part of fuzzy if-then rules are specified by partitioning each attributes into fuzzy sets while the consequent class and the degree of certainty are determined from compatibility training patterns. Extension to include a cost term is shown to be straightforward and experimental results on several image processing tasks demonstrate the efficacy of our method.