Pareto Interval Type-2 Fuzzy Decision Making for Labeled Objects

Decision making aims to select good decision options, based on ratings of utility. It is often difficult for experts to provide exact numerical ratings for decision options, so they rather prefer interval valued ratings. Interval type-2 fuzzy decision making is based on such interval valued ratings. The decision process has to balance between best case and worst case ratings, depending on the acceptable level of risk. The PIU method finds decisions by Pareto optimization of best case and worst case ratings. This paper introduces an extension of PIU to labeled objects: LPIU. Experiments with the car preference data set show that LPIU not only allows to find good decision options but also to explain how the object labels affect the decision process, an important step towards explainable AI.