# Data-driven preference learning methods for value-driven multiple criteria sorting with interacting criteria

arXiv: https://arxiv.org/abs/1905.08506 · HTML: https://mlsanity.com/paper/1905.08506/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2019.

## Built on by (most cited first, 6 of 6)

- [Recommending Multiple Criteria Decision Analysis Methods with A New Taxonomy-based Decision Support System](https://mlsanity.com/paper/2106.07378/) (2021)
- [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)
- [Lexicographic optimization-based approaches to learning a representative model for multi-criteria sorting with non-monotonic criteria](https://mlsanity.com/paper/2409.01612/) (2024)
- [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)
- [Preference Construction: A Bayesian Interactive Preference Elicitation Framework Based on Monte Carlo Tree Search](https://mlsanity.com/paper/2503.15150/) (2025)

## Abstract (arXiv)

The learning of predictive models for data-driven decision support has been a prevalent topic in many fields. However, construction of models that would capture interactions among input variables is a challenging task. In this paper, we present a new preference learning approach for multiple criteria sorting with potentially interacting criteria. It employs an additive piecewise-linear value function as the basic preference model, which is augmented with components for handling the interactions. To construct such a model from a given set of assignment examples concerning reference alternatives, we develop a convex quadratic programming model. Since its complexity does not depend on the number of training samples, the proposed approach is capable for dealing with data-intensive tasks. To improve the generalization of the constructed model on new instances and to overcome the problem of over-fitting, we employ the regularization techniques. We also propose a few novel methods for classifying non-reference alternatives in order to enhance the applicability of our approach to different datasets. The practical usefulness of the proposed method is demonstrated on a problem of parametric evaluation of research units, whereas its predictive performance is studied on several monotone learning datasets. The experimental results indicate that our approach compares favourably with the classical UTADIS method and the Choquet integral-based sorting model.