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Learning MR-Sort Models from Non-Monotone Data
arXiv - CS - Other Computer Science Pub Date : 2021-07-20 , DOI: arxiv-2107.09668
Pegdwende Minoungou, Vincent Mousseau, Wassila Ouerdane, Paolo Scotton

The Majority Rule Sorting (MR-Sort) method assigns alternatives evaluated on multiple criteria to one of the predefined ordered categories. The Inverse MR-Sort problem (Inv-MR-Sort) computes MR-Sort parameters that match a dataset. Existing learning algorithms for Inv-MR-Sort consider monotone preferences on criteria. We extend this problem to the case where the preferences on criteria are not necessarily monotone, but possibly single-peaked (or single-valley). We propose a mixed-integer programming based algorithm that learns the preferences on criteria together with the other MR-Sort parameters from the training data. We investigate the performance of the algorithm using numerical experiments and we illustrate its use on a real-world case study.

中文翻译:

从非单调数据中学习 MR-Sort 模型

多数规则排序 (MR-Sort) 方法将根据多个标准评估的备选方案分配给预定义的排序类别之一。逆 MR-Sort 问题 (Inv-MR-Sort) 计算与数据集匹配的 MR-Sort 参数。Inv-MR-Sort 的现有学习算法在标准上考虑单调偏好。我们将此问题扩展到对标准的偏好不一定是单调的,但可能是单峰(或单谷)的情况。我们提出了一种基于混合整数编程的算法,该算法从训练数据中学习标准偏好以及其他 MR-Sort 参数。我们使用数值实验研究了算法的性能,并在实际案例研究中说明了它的使用。
更新日期:2021-07-22
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