Learning MR-Sort Models from Non-Monotone Data
Abstract
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.
Cite
@article{arxiv.2107.09668,
title = {Learning MR-Sort Models from Non-Monotone Data},
author = {Pegdwende Minoungou and Vincent Mousseau and Wassila Ouerdane and Paolo Scotton},
journal= {arXiv preprint arXiv:2107.09668},
year = {2021}
}