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Assisted specification of discrete choice models
Journal of Choice Modelling ( IF 4.164 ) Pub Date : 2021-03-29 , DOI: 10.1016/j.jocm.2021.100285
Nicola Ortelli , Tim Hillel , Francisco C. Pereira , Matthieu de Lapparent , Michel Bierlaire

Determining appropriate utility specifications for discrete choice models is time-consuming and prone to errors. With the availability of larger and larger datasets, as the number of possible specifications exponentially grows with the number of variables under consideration, the analysts need to spend increasing amounts of time on searching for good models through trial-and-error, while expert knowledge is required to ensure these models are sound. This paper proposes an algorithm that aims at assisting modelers in their search. Our approach translates the task into a multi-objective combinatorial optimization problem and makes use of a variant of the variable neighborhood search algorithm to generate sets of promising model specifications. We apply the algorithm both to semi-synthetic data and to real mode choice datasets as a proof of concept. The results demonstrate its ability to provide relevant insights in reasonable amounts of time so as to effectively assist the modeler in developing interpretable and powerful models.



中文翻译:

离散选择模型的辅助规范

为离散选择模型确定适当的实用程序规格非常耗时,并且容易出错。随着越来越大的数据集的可用性,可能的规格数量随着所考虑的变量数量呈指数增长,分析师需要花费更多的时间通过反复试验来寻找良好的模型,而专家知识却是确保这些模型正确无误。本文提出了一种旨在帮助建模者进行搜索的算法。我们的方法将任务转换为多目标组合优化问题,并利用变量邻域搜索算法的变体生成有希望的模型规范集。我们将算法应用于半合成数据和实模式选择数据集,以此作为概念证明。

更新日期:2021-04-18
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