An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting

TL;DR

This paper presents an innovative incremental method for learning complex, potentially non-monotonic preferences in multi-criteria sorting problems, using max-margin models and active learning to improve decision-making accuracy.

Contribution

It introduces a novel max-margin optimization framework and active learning strategies for preference elicitation in non-monotonic multi-criteria sorting, with practical algorithms and experimental validation.

Findings

Effective in modeling non-monotonic preferences.

Improves question selection for preference elicitation.

Demonstrates superior performance over benchmarks.

Abstract

This paper introduces a novel incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting (MCS) problems, enabling decision makers to progressively provide assignment example preference information. Specifically, we first construct a max-margin optimization-based model to model potentially non-monotonic preferences and inconsistent assignment example preference information in each iteration of the incremental preference elicitation process. Using the optimal objective function value of the max-margin optimization-based model, we devise information amount measurement methods and question selection strategies to pinpoint the most informative alternative in each iteration within the framework of uncertainty sampling in active learning. Once the termination criterion is satisfied, the sorting result for non-reference alternatives can be determined through the use of two optimization models, i.e., the max-margin optimization-based model and the complexity controlling optimization model. Subsequently, two incremental preference elicitation-based algorithms are developed to learn potentially non-monotonic preferences, considering different termination criteria. Ultimately, we apply the proposed approach to a credit rating problem to elucidate the detailed implementation steps, and perform computational experiments on both artificial and real-world data sets to compare the proposed question selection strategies with several benchmark strategies.