We consider preference disaggregation in the context of multiple criteria sorting. The value function parameters and thresholds separating the classes are inferred from the Decision Maker's (DM's) assignment examples. Given the multiplicity of sorting models compatible with indirect preferences, selecting a single, representative one can be conducted differently. We review several procedures for this purpose, aiming to identify the most discriminant, average, central, benevolent, aggressive, parsimonious, or robust models. Also, we present three novel procedures that implement the robust assignment rule in practice. They exploit stochastic acceptabilities and maximize the support given to the resulting assignments by all feasible sorting models. The performance of sixteen procedures is verified on problem instances with different complexities. The results of an experimental study indicate the most efficient procedure in terms of classification accuracy, reproducing the DM's model, and delivering the most robust assignments. These include approaches identifying differently interpreted centers of the feasible polyhedron and robust methods introduced in this paper. Moreover, we discuss how the performance of all procedures is affected by different numbers of classes, criteria, characteristic points, and reference assignments. Finally, we illustrate the use of all approaches in a study concerning the assessment of the green performance of European cities.
翻译:我们考虑在多种标准分类的背景下对优惠进行分类。从决策人(DM's)的派任示例中推断出价值功能参数和分级阈值。鉴于与间接偏好相容的分类模式的多样性,可以不同地选择一个单一的代表性模式。我们为此审查了若干程序,目的是确定最不相干的、平均的、中央的、仁慈的、积极的、攻击性的、尖锐的或强有力的模式。此外,我们还介绍了在实践中执行稳健的派任规则的三个新程序。它们利用了随机的可接受性,并通过所有可行的选任模式最大限度地支持由此而来的任务分配。16项程序的绩效在复杂程度不同的问题案例中得到核实。一项实验研究的结果表明,在分类准确性、复制DM的模型和交付最有力的派任方面,最有效率的程序。这些方法包括确定各种解释不同的可行多面和稳健方法的中心。此外,我们讨论了所有程序的绩效如何受到不同种类、标准、特征和参照性指派给不同数目的欧洲城市带来何种影响。最后,我们要说明在一项绿色城市评估中采用的所有方法。