# Selection of a representative sorting model in a preference disaggregation setting: a review of existing procedures, new proposals, and experimental comparison

arXiv: https://arxiv.org/abs/2209.02410 · HTML: https://mlsanity.com/paper/2209.02410/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 67 references that are in the map.
- First posted on arXiv 30 Aug 2022.

## Builds on (most cited first, 5 of 5)

- [Data-driven preference learning methods for value-driven multiple criteria sorting with interacting criteria](https://mlsanity.com/paper/1905.08506/) (2019)
- [A preference learning framework for multiple criteria sorting with diverse additive value models and valued assignment examples](https://mlsanity.com/paper/1910.05485/) (2019)
- [An efficient SAT formulation for learning multiple criteria non-compensatory sorting rules from examples](https://mlsanity.com/paper/1710.10098/) (2017)
- [Robust stochastic sorting with interacting criteria hierarchically structured](https://mlsanity.com/paper/2003.04844/) (2020)
- [Scoring from Pairwise Winning Indices](https://mlsanity.com/paper/2107.06215/) (2021)

## Built on by (most cited first, 2 of 2)

- [Data-driven Preference Learning Methods for Sorting Problems with Multiple Temporal Criteria](https://mlsanity.com/paper/2309.12620/) (2023)
- [An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting](https://mlsanity.com/paper/2409.02760/) (2024)

## Abstract (arXiv)

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.