基于无监督学习的数据包络分析泛化

IF 3.7 4区 管理学 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE Operations Research Perspectives Pub Date : 2023-01-01 DOI:10.1016/j.orp.2023.100284
Raul Moragues , Juan Aparicio , Miriam Esteve
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引用次数: 0

摘要

在本文中,我们介绍了一种用于生产边界估计的无监督机器学习方法。这种新方法满足微观经济学的基本性质,如凸性和自由可支配性(形状约束)。该方法通过将一类支持向量机与分段线性变换映射相适应,推广了数据包络分析(DEA)。该新技术旨在减少DEA中出现的过拟合问题。介绍了如何通过定向距离函数来度量技术效率。最后,我们通过计算经验评估了新技术的性能,表明在某些情况下,前沿估计的均方误差比标准DEA高出83%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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An unsupervised learning-based generalization of Data Envelopment Analysis
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来源期刊
Operations Research Perspectives
Operations Research Perspectives Mathematics-Statistics and Probability
CiteScore
6.40
自引率
0.00%
发文量
36
审稿时长
27 days
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