A cognitive analytics management framework to select input and output variables for data envelopment analysis modeling of performance efficiency of banks using random forest and entropy of information

Author:

Bou-Hamad ImadORCID,Anouze Abdel Latef,Osman Ibrahim H.ORCID

Funder

CNRS - lEBANON

Publisher

Springer Science and Business Media LLC

Subject

Management Science and Operations Research,General Decision Sciences

Reference141 articles.

1. Adler, N., & Golany, B. (2002). Including principal component weights to improve discrimination in data envelopment analysis. Journal of the Operational Research Society, 53(9), 985–991.

2. Andor, M. A., Parmeter, C., & Sommer, S. (2019). Combining uncertainty with uncertainty to get certainty? Efficiency analysis for regulation purposes. European Journal of Operational Research, 274(1), 240–252.

3. Anouze, A. L. M., & Bou-Hamad, I. (2019). Data envelopment analysis and data mining to efficiency estimation and evaluation. International Journal of Islamic and Middle Eastern Finance and Management.

4. Arjomandi, A., Harvie, C., & Valadkhani, A. (2012). Measuring the banking efficiency and productivity changes using the Hicks-Moorsteen approach: The case of Iran.

5. Arsad, R., Abdullah, M. N., Alias, S., & Isa, Z. (2017). Selection input output by restriction using DEA models based on a fuzzy Delphi approach and expert information. Journal of Physics: Conference Series, 892(1), 012010.

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