Combining Data Envelopment Analysis and Machine Learning

Data Envelopment Analysis (DEA) is one of the most used non-parametric techniques for technical efficiency assessment. DEA is exclusively concerned about the minimization of the empirical error, satisfying, at the same time, some shape constraints (convexity and free disposability). Unfortunately, b...

Descripción completa

Detalles Bibliográficos
Autores: Guerrero, Nadia María, Aparicio, Juan, Valero Carreras, Daniel
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universidad Católica San Antonio de Murcia (UCAM)
Repositorio:RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murcia
OAI Identifier:oai:repositorio.ucam.edu:10952/9046
Acceso en línea:http://hdl.handle.net/10952/9046
Access Level:acceso abierto
Palabra clave:Data envelopment analysis
PAC learning
Support vector regression
Machine learning
Structural risk minimization
id ES_a9065c48bd4d2f4bb21acb4d62d7de8e
oai_identifier_str oai:repositorio.ucam.edu:10952/9046
network_acronym_str ES
network_name_str España
repository_id_str
spelling Combining Data Envelopment Analysis and Machine LearningGuerrero, Nadia MaríaAparicio, JuanValero Carreras, DanielData envelopment analysisPAC learningSupport vector regressionMachine learningStructural risk minimizationData Envelopment Analysis (DEA) is one of the most used non-parametric techniques for technical efficiency assessment. DEA is exclusively concerned about the minimization of the empirical error, satisfying, at the same time, some shape constraints (convexity and free disposability). Unfortunately, by construction, DEA is a descriptive methodology that is not concerned about preventing overfitting. In this paper, we introduce a new methodology that allows for estimating polyhedral technologies following the Structural Risk Minimization (SRM) principle. This technique is called Data Envelopment Analysis-based Machines (DEAM). Given that the new method controls the generalization error of the model, the corresponding estimate of the technology does not suffer from overfitting. Moreover, the notion of ε-insensitivity is also introduced, generating a new and more robust definition of technical efficiency. Additionally, we show that DEAM can be seen as a machine learning-type extension of DEA, satisfying the same microeconomic postulates except for minimal extrapolation. Finally, the performance of DEAM is evaluated through simulations. We conclude that the frontier estimator derived from DEAM is better than that associated with DEA. The bias and mean squared error obtained for DEAM are smaller in all the scenarios analyzed, regardless of the number of variables and DMUs.Ingeniería, Industria y ConstrucciónEscuela Politécnica2022info:eu-repo/semantics/articlehttp://hdl.handle.net/10952/9046reponame:RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murciainstname:Universidad Católica San Antonio de Murcia (UCAM)Inglésinfo:eu-repo/semantics/openAccessoai:repositorio.ucam.edu:10952/90462026-06-07T18:35:21Z
dc.title.none.fl_str_mv Combining Data Envelopment Analysis and Machine Learning
title Combining Data Envelopment Analysis and Machine Learning
spellingShingle Combining Data Envelopment Analysis and Machine Learning
Guerrero, Nadia María
Data envelopment analysis
PAC learning
Support vector regression
Machine learning
Structural risk minimization
title_short Combining Data Envelopment Analysis and Machine Learning
title_full Combining Data Envelopment Analysis and Machine Learning
title_fullStr Combining Data Envelopment Analysis and Machine Learning
title_full_unstemmed Combining Data Envelopment Analysis and Machine Learning
title_sort Combining Data Envelopment Analysis and Machine Learning
dc.creator.none.fl_str_mv Guerrero, Nadia María
Aparicio, Juan
Valero Carreras, Daniel
author Guerrero, Nadia María
author_facet Guerrero, Nadia María
Aparicio, Juan
Valero Carreras, Daniel
author_role author
author2 Aparicio, Juan
Valero Carreras, Daniel
author2_role author
author
dc.subject.none.fl_str_mv Data envelopment analysis
PAC learning
Support vector regression
Machine learning
Structural risk minimization
topic Data envelopment analysis
PAC learning
Support vector regression
Machine learning
Structural risk minimization
description Data Envelopment Analysis (DEA) is one of the most used non-parametric techniques for technical efficiency assessment. DEA is exclusively concerned about the minimization of the empirical error, satisfying, at the same time, some shape constraints (convexity and free disposability). Unfortunately, by construction, DEA is a descriptive methodology that is not concerned about preventing overfitting. In this paper, we introduce a new methodology that allows for estimating polyhedral technologies following the Structural Risk Minimization (SRM) principle. This technique is called Data Envelopment Analysis-based Machines (DEAM). Given that the new method controls the generalization error of the model, the corresponding estimate of the technology does not suffer from overfitting. Moreover, the notion of ε-insensitivity is also introduced, generating a new and more robust definition of technical efficiency. Additionally, we show that DEAM can be seen as a machine learning-type extension of DEA, satisfying the same microeconomic postulates except for minimal extrapolation. Finally, the performance of DEAM is evaluated through simulations. We conclude that the frontier estimator derived from DEAM is better than that associated with DEA. The bias and mean squared error obtained for DEAM are smaller in all the scenarios analyzed, regardless of the number of variables and DMUs.
publishDate 2022
dc.date.none.fl_str_mv 2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10952/9046
url http://hdl.handle.net/10952/9046
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murcia
instname:Universidad Católica San Antonio de Murcia (UCAM)
instname_str Universidad Católica San Antonio de Murcia (UCAM)
reponame_str RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murcia
collection RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murcia
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869415966671110144
score 15,812429

Similares en LA Referencia