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J. Garibaldi - Non-Stationary Fuzzy Sets in Medical Decision Making

Fuzzy sets were introduced by Zadeh in the 1960s, and were subsequently expanded into a complete systematic framework for dealing with uncertainty. As part of the generic fuzzy methodologies, fuzzy inference systems were proposed for the modelling of human reasoning with uncertain data and knowledge. However, standard fuzzy sets and fuzzy reasoning do not model the variability in decision making that is typically exhibited by all human experts in any domain. Variation may occur among the decisions of a panel of human experts (inter-expert variability), as well as in the decisions of an individual expert over time (intra-expert variability).

Dr Garibaldi has introduced the concept of non-stationary fuzzy sets, in which small changes (perturbations) are introduced in the membership functions associated with the linguistic terms of a fuzzy inference system. These small changes mean that each time a fuzzy inference system is run with the same data, a different result is obtained. It is straight-forward to extend this notion to create an ensemble fuzzy inference system featuring non-stationary fuzzy sets. In this talk (aimed at an audience not completely familiar with fuzzy methods), non-stationary fuzzy sets and reasoning will be explained in detail, and its use in several real-world scenarios of decision support in medical contexts will be described. Results will be presented to demonstrate the benefits of non-stationary fuzzy reasoning.