Feature Selection in Data Envelopment Analysis: A Mathematical Optimization approach

被引：17

作者：

Benitez-Pena, Sandra ^{[1
]}

Bogetoft, Peter ^{[2
]}

Morales, Dolores Romero ^{[2
]}

机构：

[1] Univ Seville, Inst Matemat, IMUS, Seville, Spain

[2] Copenhagen Business Sch, Frederiksberg, Denmark

来源：

OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE | 2020年 / 96卷 / 96期

基金：

欧盟地平线“2020”;

关键词：

Benchmarking; Data Envelopment Analysis; Feature Selection; Mixed Integer Linear Programming; VARIABLE SELECTION; DEA MODELS; EFFICIENCY; RANKING; WEIGHTS; BENCHMARKING;

D O I：

10.1016/j.omega.2019.05.004

中图分类号：

C93 [管理学];

学科分类号：

12 ; 1201 ; 1202 ; 120202 ;

摘要：

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.

引用

页数：11

共 39 条

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