通过嵌入结合离散选择模型和神经网络：公式化、可解释性和性能
Combining Discrete Choice Models and Neural Networks through Embeddings: Formulation, Interpretability and Performance
摘要：
本研究提出了一种新的方法，结合理论和数据驱动的选择模型，使用人工神经网络（ANN）。特别是，我们使用连续向量表示，称为嵌入，用于编码分类或离散解释变量，特别关注可解释性和模型透明度。虽然Pereira（2019）已经概念化了logit框架中的嵌入表示，但它们的维度并没有绝对的确定意义，因此在这项早期工作中提供了有限的行为见解。我们的工作的新颖性在于执行可解释性的嵌入向量正式关联的每个维度的选择。因此，我们的方法带来的好处远远超出了一个简单的吝啬表示改进虚拟编码，因为它提供了行为上有意义的输出，可用于旅游需求分析和政策决策。此外，与先前建议的基于人工神经网络的离散选择模型（DCM）相比，该模型要么牺牲性能的可解释性，要么仅部分可解释，尽管基于人工神经网络原理，但我们的模型保留了所有输入变量的效用系数的可解释性。在两个真实世界的数据集上对所提出的模型进行了测试，并对使用虚拟编码的基准和基线模型进行了评估。实验结果表明，我们的模型提供了最先进的预测性能，优于现有的基于人工神经网络的模型，同时大大减少了所需的网络参数的数量。
英文摘要：
This study proposes a novel approach that combines theory and data-driven choice models using Artificial Neural Networks (ANNs). In particular, we use continuous vector representations, called embeddings, for encoding categorical or discrete explanatory variables with a special focus on interpretability and model transparency. Although embedding representations within the logit framework have been conceptualized by Pereira (2019), their dimensions do not have an absolute definitive meaning, hence offering limited behavioral insights in this earlier work. The novelty of our work lies in enforcing interpretability to the embedding vectors by formally associating each of their dimensions to a choice alternative. Thus, our approach brings benefits much beyond a simple parsimonious representation improvement over dummy encoding, as it provides behaviorally meaningful outputs that can be used in travel demand analysis and policy decisions. Additionally, in contrast to previously suggested ANN-based Discrete Choice Models (DCMs) that either sacrifice interpretability for performance or are only partially interpretable, our models preserve interpretability of the utility coefficients for all the input variables despite being based on ANN principles. The proposed models were tested on two real world datasets and evaluated against benchmark and baseline models that use dummy-encoding. The results of the experiments indicate that our models deliver state-of-the-art predictive performance, outperforming existing ANN-based models while drastically reducing the number of required network parameters.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
