Title: An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting Show Chinese title

Title: 基于增量式偏好获取的多准则分类中潜在非单调偏好的学习方法

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. Show Chinese abstract

Abstract: 本文介绍了一种新颖的基于增量式偏好获取的方法，用于学习多准则分类（MCS）问题中潜在非单调的偏好，使决策者能够逐步提供分配示例偏好信息。 具体而言，我们首先构建了一个基于最大间隔优化的模型，以建模增量式偏好获取过程中每次迭代中潜在的非单调偏好和不一致的分配示例偏好信息。 利用基于最大间隔优化模型的最优目标函数值，我们设计了信息量度量方法和问题选择策略，在主动学习的不确定性采样框架内确定每次迭代中最具信息量的备选方案。 一旦满足终止标准，可以通过两种优化模型（即基于最大间隔优化的模型和复杂性控制优化模型）来确定非参考备选方案的分类结果。 随后，开发了两种基于增量式偏好获取的算法，以考虑不同的终止标准来学习潜在的非单调偏好。 最终，我们将所提出的方法应用于信用评级问题以阐明详细实现步骤，并在人工数据集和真实数据集上进行计算实验，以比较所提出的提问选择策略与几种基准策略。