Feature Selection in Data Envelopment Analysis: A Mathematical Optimization approach
Feature Selection in Data Envelopment Analysis: A Mathematical Optimization approach
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DOI：
10.1016/j.omega.2019.05.004
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发表时间：
2020-10-01
影响因子：
6.9
通讯作者：
Morales, Dolores Romero
中科院分区：
文献类型：
--
作者：
Benitez-Pena, Sandra;Bogetoft, Peter;Morales, Dolores Romero
本文提出了一种数据包络分析（DEA）中特征（输入和输出）选择的综合方法。的DEA模型是丰富的零-一决策变量建模的选择功能，产生一个混合线性规划公式。这种单模型方法可以处理不同的目标函数以及约束，以结合现实世界的应用程序所需的属性。我们的方法是说明对电力配电系统运营商（DSO）的基准。数值结果突出了我们的单模型方法提供给用户的优势，在选择功能的数量，以及建模的成本和它们的性质。(C)2019爱思唯尔有限公司版权所有。
This paper proposes an integrative approach to feature (input and output) selection in Data Envelopment Analysis (DEA). The DEA model is enriched with zero-one decision variables modelling the selection of features, yielding a Mixed Integer Linear Programming formulation. This single-model approach can handle different objective functions as well as constraints to incorporate desirable properties from the real-world application. Our approach is illustrated on the benchmarking of electricity Distribution System Operators (DSOs). The numerical results highlight the advantages of our single-model approach provide to the user, in terms of making the choice of the number of features, as well as modeling their costs and their nature. (C) 2019 Elsevier Ltd. All rights reserved.