基于深度神经网络的建筑设计选择分析 替代特定效用函数

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摘要翻译：
尽管深度神经网络(DNN)越来越多地应用于选择分析，但如何协调特定领域的行为知识和通用DNN，提高DNN的可解释性和预测能力，以及为特定任务识别有效的正则化方法是一个挑战。本研究利用先验行为知识，设计了一个具有替代特定效用函数的DNN体系结构（ASU-DNN）。与完全连通DNN（F-DNN）不同，ASU-DNN只使用k的属性来计算k的效用值，而完全连通DNN使用k的属性来计算k的效用值。理论上，ASU-DNN由于其结构更轻，连通性更稀疏，可以显著减小F-DNN的估计误差。在新加坡收集的私有数据集和R mlogit软件包中的公共数据集上，ASU-DNN在整个超参数空间上的预测精度比F-DNN高2-3%。替代特定连通性约束作为一种基于领域知识的正则化方法，比目前最流行的通用目的显式和隐式正则化方法以及体系结构超参数更有效。ASU-DNN的解释性也更强，因为它比F-DNN提供了更有规律的出行方式选择替代模式。ASU-DNN与F-DNN的比较也有助于行为知识的检验。我们的结果表明，个人更有可能通过使用替代方案自己的属性来计算效用，这支持了选择建模中的长期实践。总体而言，本研究表明，先验行为知识可以用来指导DNN的体系结构设计，作为一种有效的基于领域知识的正则化方法，提高DNN在选择分析中的可解释性和预测能力。
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英文标题：
《Deep Neural Networks for Choice Analysis: Architectural Design with
  Alternative-Specific Utility Functions》
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作者：
Shenhao Wang, Baichuan Mo, Jinhua Zhao
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最新提交年份：
2021
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Economics        经济学
二级分类：General Economics        一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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英文摘要：
  Whereas deep neural network (DNN) is increasingly applied to choice analysis, it is challenging to reconcile domain-specific behavioral knowledge with generic-purpose DNN, to improve DNN's interpretability and predictive power, and to identify effective regularization methods for specific tasks. This study designs a particular DNN architecture with alternative-specific utility functions (ASU-DNN) by using prior behavioral knowledge. Unlike a fully connected DNN (F-DNN), which computes the utility value of an alternative k by using the attributes of all the alternatives, ASU-DNN computes it by using only k's own attributes. Theoretically, ASU-DNN can dramatically reduce the estimation error of F-DNN because of its lighter architecture and sparser connectivity. Empirically, ASU-DNN has 2-3% higher prediction accuracy than F-DNN over the whole hyperparameter space in a private dataset that we collected in Singapore and a public dataset in R mlogit package. The alternative-specific connectivity constraint, as a domain-knowledge-based regularization method, is more effective than the most popular generic-purpose explicit and implicit regularization methods and architectural hyperparameters. ASU-DNN is also more interpretable because it provides a more regular substitution pattern of travel mode choices than F-DNN does. The comparison between ASU-DNN and F-DNN can also aid in testing the behavioral knowledge. Our results reveal that individuals are more likely to compute utility by using an alternative's own attributes, supporting the long-standing practice in choice modeling. Overall, this study demonstrates that prior behavioral knowledge could be used to guide the architecture design of DNN, to function as an effective domain-knowledge-based regularization method, and to improve both the interpretability and predictive power of DNN in choice analysis.
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PDF链接：
https://arxiv.org/pdf/1909.07481

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关键词：建筑设计 神经网络 效用函数 神经网 Architecture behavioral using more 新加坡 regularization