Pareto Interval Type-2 Fuzzy Decision Making for Labeled Objects

2022 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE)(2022)

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摘要
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.
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关键词
labeled objects,exact numerical ratings,interval valued ratings,worst case ratings,Pareto optimization,Pareto interval type-2 fuzzy decision making,utility rating,best case rating,worst case rating,PIU method,car preference data set,explainable AI
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