.png){kind=icon}
.png){kind=icon}
Return
Feature Selection in Data Envelopment Analysis: A Mathematical Optimization approach
.png){kind=icon}
OA
AI
DOI：10.1016/j.omega.2019.05.004.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Benchmarking
Data Envelopment Analysis
Feature Selection
Mixed Integer Linear Programming
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
O
IF:
7.2
Papers:
3.7K
Citations:
1.4W