Data-driven preference learning methods for value-driven multiple criteria sorting with interacting criteria
Data-driven preference learning methods for value-driven multiple criteria sorting with interacting criteria
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用于价值驱动的具有交互标准的多标准排序的数据驱动偏好学习方法
DOI：
10.1287/ijoc.2020.0977
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通讯作者：
Xiaoxin Mao
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作者：
Jiapeng Liu;Miłosz Kadziński;Xiuwu Liao;Xiaoxin Mao
用于数据驱动决策支持的预测模型的学习一直是许多领域的热门话题。然而，构建能够捕捉输入变量之间相互作用的模型是一项具有挑战性的任务。针对潜在交互准则的多准则排序问题，提出了一种新的偏好学习方法。它使用了一个附加的分段线性值函数作为基本偏好模型，并增加了处理交互的组件。为了从一组与参考方案有关的分配实例中构造这样的模型，我们建立了一个凸二次规划模型。由于其复杂性不依赖于训练样本的数量，因此该方法能够处理数据密集型任务。为了提高所建模型对新实例的泛化能力，克服过拟合度问题，我们采用了正则化技术。我们还提出了一些新的方法来分类非参考备选方案，以增强我们的方法对不同数据集的适用性。在一个研究单元参数评价问题上验证了该方法的实用性，并在几个单调分类问题上研究了该方法的预测性能。实验结果表明，该方法优于经典的S加性判别法和基于Choquite积分的排序模型。稿件摘要。这篇论文解决了多准则决策分析和机器学习的交叉点上的关键挑战，展示了如何使用计算先进的技术来忠实地表示人类的偏好和处理复杂的决策问题。针对多准则排序问题，提出了一种新的偏好学习方法。该方法结合了凸二次规划方法，在大量偏好语句的基础上构造了一个基于值的偏好模型。通过这种方式，我们将决策分析方法的适用性扩展到从历史数据或对用户行为的观察得出的偏好，以及决策者明确显示的偏好判断。该方法在高等教育、医学、人力资源和住房市场等领域的各种真实数据集上得到了实际应用。它支持更好决策的潜力得到了增强，既有处理标准之间相互作用的假设模型的可解释形式，也有在广泛的计算实验中展示的高预测性能。
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. Because 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 overfitting, we employ the regularization techniques. We also propose a few novel methods for classifying nonreference alternatives in order to enhance the applicability of our approach to different data sets. The practical usefulness of the proposed approach is demonstrated on a problem of parametric evaluation of research units, whereas its predictive performance is studied on several monotone classification problems. The experimental results indicate that our approach compares favourably with the classical UTilités Additives DIScriminantes (UTADIS) method and the Choquet integral-based sorting model. Summary of Contribution. The paper tackles vital challenges at the intersections of multiple criteria decision analysis and machine learning, showing how computationally advanced techniques can be used for faithfully representing human preferences and dealing with complex decision problems. Specifically, we propose a novel preference learning method for multiple criteria sorting problems. The introduced approach incorporates convex quadratic programming to construct a value-based preference model based on large sets of preference statements. In this way, we extend the applicability of decision analysis methods to preferences derived from historical data or observation of users' behavior in addition to the preference judgments explicitly revealed by the decision-makers. The method's practical usefulness is illustrated on a variety of real-world datasets from fields such as higher education, medicine, human resources, and housing market. Its potential for supporting better decision-making is enhanced by both an interpretable form of the assumed model handling interactions between criteria as well as a high predictive performance demonstrated in the extensive computational experiments.