Input/output variables selection in data envelopment analysis: A shannon entropy approach

Peykani, Pejman, Seyed Esmaeili, Fatemeh Sadat, Mirmozaffari, Mirpouya, Jabbarzadeh, Armin et Khamechian, Mohammad. 2022. « Input/output variables selection in data envelopment analysis: A shannon entropy approach ». Machine Learning and Knowledge Extraction, vol. 4, nº 3. pp. 688-699.
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Résumé

The purpose of this study is to provide an efficient method for the selection of input–output indicators in the data envelopment analysis (DEA) approach, in order to improve the discriminatory power of the DEA method in the evaluation process and performance analysis of homogeneous decision-making units (DMUs) in the presence of negative values and data. For this purpose, the Shannon entropy technique is used as one of the most important methods for determining the weight of indicators. Moreover, due to the presence of negative data in some indicators, the range directional measure (RDM) model is used as the basic model of the research. Finally, to demonstrate the applicability of the proposed approach, the food and beverage industry has been selected from the Tehran stock exchange (TSE) as a case study, and data related to 15 stocks have been extracted from this industry. The numerical and experimental results indicate the efficacy of the hybrid data envelopment analysis–Shannon entropy (DEASE) approach to evaluate stocks under negative data. Furthermore, the discriminatory power of the proposed DEASE approach is greater than that of a classical DEA model

Type de document:	Article publié dans une revue, révisé par les pairs
Professeur:	Professeur Jabbarzadeh, Armin
Affiliation:	Génie des systèmes
Date de dépôt:	17 oct. 2022 14:02
Dernière modification:	15 nov. 2022 14:02
URI:	https://espace2.etsmtl.ca/id/eprint/25659

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