Incorporating Domain Knowledge in Deep Neural Networks for Discrete Choice Models
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在离散选择模型的深度神经网络中融入领域知识
- 研究问题
- 如何在离散选择分析中保留机器学习拟合非线性关系的灵活性，同时让模型遵循领域知识和可解释的行为关系？
- 方法与思路
- 作者用伪数据样本表达预期关系，并在训练损失中衡量这些关系的满足程度，与观测数据共同训练深度神经网络。
- 材料陈述的贡献
- 摘要提出通过约束融入领域知识的建模框架，旨在结合机器学习的设定灵活性与行为可解释性，并用案例展示潜力。
- 阅读关注点
- 应核对伪样本如何编码行为关系、约束损失与数据拟合如何权衡，以及约束在未观测选择情境中是否仍成立。
- 阅读边界
- 所给版本为 arXiv 预印本，不能确认同行评审或期刊接收；摘要未提供案例数据和比较数值，也未证明约束全局成立。
原文摘要
Discrete choice models (DCM) are widely employed in travel demand analysis as a powerful theoretical econometric framework for understanding and predicting choice behaviors. DCMs are formed as random utility models (RUM), with their key advantage of interpretability. However, a core requirement for the estimation of these models is a priori specification of the associated utility functions, making them sensitive to modelers' subjective beliefs. Recently, machine learning (ML) approaches have emerged as a promising avenue for learning unobserved non-linear relationships in DCMs. However, ML models are considered "black box" and may not correspond with expected relationships. This paper proposes a framework that expands the potential of data-driven approaches for DCM by supporting the development of interpretable models that incorporate domain knowledge and prior beliefs through constraints. The proposed framework includes pseudo data samples that represent required relationships and a loss function that measures their fulfillment, along with observed data, for model training. The developed framework aims to improve model interpretability by combining ML's specification flexibility with econometrics and interpretable behavioral analysis. A case study demonstrates the potential of this framework for discrete choice analysis.