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

TL;DR

This paper introduces a SAT-based formulation for learning non-compensatory sorting rules in MCDA, offering guaranteed consistency with the learning set and improved computational efficiency over existing methods.

Contribution

It presents a novel SAT formulation for NCS model learning that ensures perfect fit to data and is more scalable than traditional mixed integer programming approaches.

Findings

The SAT-based approach guarantees to find a fully consistent NCS model if one exists.

It is significantly more computationally efficient than existing MIP methods.

The method effectively handles larger datasets in MCDA sorting problems.

Abstract

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.