Combining Discrete Choice Models and Neural Networks through Embeddings: Formulation, Interpretability and Performance
通过嵌入结合离散选择模型与神经网络：形式、可解释性及表现AI 解读 ↓
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通过嵌入结合离散选择模型与神经网络：形式、可解释性及表现
- 研究问题
- 论文研究如何结合离散选择理论与神经网络，用低维嵌入表示类别变量，同时保留效用系数的行为解释并提升预测表现。
- 方法与思路
- 作者将嵌入向量的各维度正式关联到选择备选项，在结合神经网络与选择模型的框架内编码离散解释变量，并比较实际数据表现。
- 材料陈述的贡献
- 摘要称该结构保留所有输入变量效用系数的可解释性；两组真实数据实验报告较强预测表现，并显著减少所需网络参数。
- 阅读关注点
- 阅读全文时应核对嵌入与备选项的对应约束、效用系数的识别和解释方式，以及样本划分、基准调参和政策分析的适用条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；摘要未给出预测差异和识别细节，系数可解释性本身不能保证政策效应具有因果含义。
原文摘要
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.