Multicriteria sorting methods are often used in decision aiding contexts where the objective is to assign alternatives to predefined ordered categories. The Electre Tri family of sorting methods is based on pairwise comparisons of the alternatives with some, possibly fictional, alternatives that are either upper or lower limits of the categories (Electre Tri-B), or one or more typical reference alternatives, that is, representative categories profiles (Electre Tri-C, Tri-nC). In this paper, we are interested in the Electre Tri-nC method and in indirect preference elicitation based on partial information provided by the Decision Maker. We therefore propose, apply and evaluate a preference disaggregation method for learning criteria weights and the credibility threshold used in Electre Tri-nC. The proposed disaggregation method is validated in an experiment using a climate classification problem for light tourism where 62,482 touristic locations are sorted into four categories. A robustness analysis of the method's performance using 150 learning sets is conducted and the results are presented and discussed.

中文翻译：

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Electre TRI-nC 中间接偏好引出的分解方法：应用和验证

多标准排序方法通常用于决策辅助上下文，其目标是为预定义的有序类别分配替代方案。Electre Tri 系列排序方法基于将备选方案与某些可能是虚构的备选方案（可能是类别的上限或下限）或一个或多个典型参考备选方案的成对比较，即，代表性类别配置文件（Electre Tri-C、Tri-nC）。在本文中，我们对 Electre Tri-nC 方法和基于决策者提供的部分信息的间接偏好获取感兴趣。因此，我们提出、应用和评估了一种用于学习标准权重和 Electre Tri-nC 中使用的可信度阈值的偏好分解方法。提议的分解方法在使用气候分类问题的轻型旅游实验中得到验证，其中 62,482 个旅游地点被分为四类。使用 150 个学习集对该方法的性能进行了稳健性分析，并对结果进行了介绍和讨论。