# An efficient SAT formulation for learning multiple criteria non-compensatory sorting rules from examples

arXiv: https://arxiv.org/abs/1710.10098 · HTML: https://mlsanity.com/paper/1710.10098/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2017.

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

- [Selection of a representative sorting model in a preference disaggregation setting: a review of existing procedures, new proposals, and experimental comparison](https://mlsanity.com/paper/2209.02410/) (2022)
- [Learning MR-Sort Models from Non-Monotone Data](https://mlsanity.com/paper/2107.09668/) (2021)

## Abstract (arXiv)

The literature on Multiple Criteria Decision Analysis (MCDA) proposes several methods in order to sort alternatives evaluated on several attributes into ordered classes. Non Compensatory Sorting models (NCS) assign alternatives to classes based on the way they compare to multicriteria profiles separating the consecutive classes. Previous works have proposed approaches to learn the parameters of a NCS model based on a learning set. Exact approaches based on mixed integer linear programming ensures that the learning set is best restored, but can only handle datasets of limited size. Heuristic approaches can handle large learning sets, but do not provide any guarantee about the inferred model. In this paper, we propose an alternative formulation to learn a NCS model. This formulation, based on a SAT problem, guarantees to find a model fully consistent with the learning set (whenever it exists), and is computationally much more efficient than existing exact MIP approaches.