Machine learning Clustering Algorithms Based on the DEA Optimization Approach for Banking System in Developing Countries


  •   Mirpouya Mirmozaffari

  •   Azam Boskabadi

  •   Gohar Azeem

  •   Reza Massah

  •   Elahe Boskabadi

  •   Hamidreza Ahady Dolatsara

  •   Ata Liravian


Machine learning grows quickly, which has made numerous academic discoveries and is extensively evaluated in several areas. Optimization, as a vital part of machine learning, has fascinated much consideration of practitioners. The primary purpose of this paper is to combine optimization and machine learning to extract hidden rules, remove unrelated data, introduce the most productive Decision-Making Units (DMUs) in the optimization part, and to introduce the algorithm with the highest accuracy in Machine learning part. In the optimization part, we evaluate the productivity of 30 banks from eight developing countries over the period 2015-2019 by utilizing Data Envelopment Analysis (DEA). An additive Data Envelopment Analysis (DEA) model for measuring the efficiency of decision processes is used. The additive models are often named Slack Based Measure (SBM). This group of models measures efficiency via slack variables. After applying the proposed model, the Malmquist Productivity Index (MPI) is computed to evaluate the productivity of companies. In the machine learning part, we use a specific two-layer data mining filtering pre-processes for clustering algorithms to increase the efficiency and to find the superior algorithm. This study tackles data and methodology-related issues in measuring the productivity of the banks in developing countries and highlights the significance of DMUs productivity and algorithms accuracy in the banking industry by comparing suggested models.

Keywords: Machine Learning, Optimization, Data Envelopment Analysis, Data Mining, Clustering, Cross-Efficiency, Banking System


Z Svitalkova, Comparison and evaluation of bank efficiency in selected countries in EU. Procedia Economics and Finance, 12(1), 644–653, 2014.

P Wanke, M. A. K Azad and C Barros. Predicting efficiency in Malaysian Islamic banks: A two-stage TOPSIS and neural networks approach. Research in International Business and Finance, 36(January (1)), 485–498, 2016.

A Charnes, W Cooper, E Rhodes, Measuring the efficiency of decision-making units. European Journal of Operational Research, 2(November (6)), 429–444, 1978.

A Charnes, W. W., Cooper, A. Y Lewin and L.M. Seiford, Data Envelopment Analysis: Theory, methodology, and applications. Springer, 1994.

I. E. Tsolas, and V Charles, incorporating risk into bank efficiency: A satisficing DEA approach to assess the Greek banking crisis. Expert Systems with Applications, 42(May (7)), 3491–3500, 2015.

P Schure, R Wagenvoort, and D OB¨rien, The efficiency and the conduct of Europe on banks: Developments after 1992. Review of Financial Economics, 13(January (4)), 371–396, 2004.

I. Řepková. Efficiency of the Czech banking sector employing the DEA window analysis approach. Procedia Economics and Finance, 12(1), 587–596, 2014.

T. T. Lin, C.C Lee, and T.F. Chiu. Application of DEA in analyzing a bank operating performance. Expert Systems with Applications, 36(July (5)), 8883–8891, 2009.

Y. Luo, G. Bi, L. Liang, Input/output indicator selection for DEA efficiency evaluation: An empirical study of Chinese commercial banks. Expert Systems with Applications, 39 (January (1)), 1118–1123, 2012.

S.T. Liu Measuring and categorizing technical efficiency and productivity change of commercial banks in Taiwan. Expert Systems with Applications, 37(April (4)), 2783–2789, 2010.

A. Sokic, Cost efficiency of the banking industry and unilateral euroization: A stochastic frontier approach in Serbia and Montenegro. Economic Systems, 39(September (3)), 541–551, 2015.

F. Kamarudin, F Sufian, and A.M Nassir, Does country governance foster revenue efficiency of Islamic and conventional banks in GCC countries? EuroMed Journal of Business, 11(2), 181–211, 2016.

F. Kamarudin, F Sufian, F.W. Loong, and N. A. M. Anwar, Assessing the Domestic and Foreign Islamic Banks Efficiency: Insights from Selected Southeast Asian countries. Future Business Journal, 3(1), 33–46, 2017.

A. N. Berger, and D. B. Humphrey. Efficiency of financial institutions: International survey and directions for future research. European Journal of Operational Research, 1, 1, 1997.

M. Mirmozaffari, “Eco-Efficiency Evaluation in Two-Stage Network Structure: Case Study: Cement Companies,” Iranian Journal of Optimization (IJO), Dec. 16, 2018.

M. Mirmozaffari, and A. Alinezhad, “Ranking of Heart Hospitals Using cross-efficiency and two-stage DEA,” 7th International Conference on Computer and Knowledge Engineering (ICCKE), Mashhad, pp. 217-222, 2017.

A. Alinezhad, and M. Mirmozaffari, “Malmquist Productivity Index Using Two-stage DEA Model in Heart Hospital, Iranian Journal of Optimization. Volume 10, Issue 2, 2018.

M. Mirmozaffari, “Presenting an expert system for early diagnosis of gastrointestinal diseases,” International Journal of Gastroenterology Sciences, Vol 1; Issue 1; Page 21-27, 2020.

M. Mirmozaffari, “Developing an Expert System for Diagnosing Liver Diseases,” EJERS, vol. 4, no. 3, pp. 1-5, Mar. 2019.

M. Mirmozaffari, “Presenting a Medical Expert System for Diagnosis and Treatment of Nephrolithiasis,” EJMED. May; 1:1, 2019.

A. Aranizadeh, M. Kazemi, H. Berahmandpour, and M. Mirmozaffari, “MULTIMOORA Decision Making Algorithm for Expansion of HVDC and EHVAC in Developing Countries (A Case Study),” Iranian Journal of Optimization, 2020.

A. Aranizadeh, I. Niazazari, and M. Mirmozaffari, “A Novel Optimal Distributed Generation Planning in Distribution Network using Cuckoo Optimization Algorithm,” European Journal of Electrical Engineering and Computer Science 3 (3), 2019.

A. Azadeh, A. Boskabadi, and S. Pashapour. “A unique support vector regression for improved modelling and forecasting of short-term gasoline consumption in railway systems,” International Journal of Services and Operations Management, 21(2), 217-237, 2015.

A. Boskabadi. “Using support vector regression (SVR) for weekly oil consumption prediction in railway transportation industry,” no. December 1-12, 2011.

L. Drake, M.J. Hall, and R. Simper, Bank modelling methodologies: A comparative nonparametric analysis of efficiency in the Japanese banking sector. Journal of International Financial Markets, Institutions and Money, 19(February (1)), 1–15, 2009.

G.J. Benston, Branch banking and economies of scale. The Journal of Finance, 20(May (2)), 312–331, 1965.

C. W. Sealey, and J. T. Lindley. Inputs, outputs and a theory of production and cost at depository financial institutions. The Journal of Finance, 32(September (4)), 1251–1266, 1977.

E. Zimková. Technical efficiency and super-efficiency of the banking sector in Slovakia. Procedia Economics and Finance, 12(1), 780–787,2014.

G.A Assaf, C.P. Barros, C. P., and R Matousek, Technical efficiency in Saudi banks. Expert Systems with Applications, 38 (May (5)), 5781–5786, 2011.

A. Yilmaz, and N. Güneş. Efficiency comparison of participation and conventional banking sectors in Turkey between 2007–2013. Procedia - Social and Behavioral Sciences, 195(July (1)), 383–392, 2015.

A.I. Ali, and L.M. Seiford, Computational accuracy and infinitesimals in data envelopment analysis. INFOR 31 (4), 290–297, 1993.

S. Mehrabian, G.R. Jahanshahloo, M.R. Alirezaee, G.R. Amin. An assurance interval for the non-Archimedean epsilon in DEA models. Operations Research 48 (2), :344–347, 2000.

T.R. Sexton, R.H. Silkman, A.J. and Hogan. Data envelopment analysis: critique and extensions. In: Silkman, R.H. (Ed.), Measuring Efficiency: An Assessment of Data Envelopment Analysis. Jossey-Bass, Francisco, CA,1986.

J. Doyle, and R. Green. Efficiency and cross-efficiency in DEA: derivations, meanings and uses. Journal of the Operations Research Society 45 (5), 567–578,1994.

A. Boskabadi and A. Azadeh “A fuzzy model for a distribution network problem in a multi-product supply chain system,” 5th national & 3rd international LOGESTICS & SUPPLY CHAIN CONFERENCE, 75-85, 2012.

H. Kamalzadeh, S.N. Sobhan, A. Boskabadi, M. Hatami and A. Gharehyakheh, “Modeling and Prediction of Iran's Steel Consumption Based on Economic Activity Using Support Vector Machines,” arXiv preprint arXiv:1912.02373, 2019.

S.S..Fazeli, S. Venkatachalam, R.B. Chinnam, and A. “Murat. Two-Stage Stochastic Choice Modeling Approach for Electric Vehicle Charging Station Network Design in Urban Communities,” IEEE Transactions on Intelligent Transportation Systems, 2020.

M. Mirmozaffari, G. Azeem, A. Boskabadi, A. Aranizadeh, A. Vaishnav, and J. John, “A Novel Improved Data Envelopment Analysis Model Based on SBM and FDH Models”, EJECE, vol. 4, no. 3, May 2020.

S. Amin-Nejad, T.A. Gashteroodkhani, and Basharkhah, K., "A Comparison of MVDR and LCMV Beamformers’ Floating Point Implementations on FPGAs," Wireless Personal Communications, vol. 98, no. 2, pp.1913-1929, 2018.

S. Amin-Nejad, K. Basharkhah, and T.A. Gashteroodkhani "Floating Point versus Fixed point Tradeoffs in FPGA Implementations of QR Decomposition Algorithm," European Journal of Electrical and Computer Engineering, vol. 3, no. 5, 2019.

O.A. Gashteroodkhani, M. Majidi, M. Etezadi-Amoli “A Fuzzy-based Control Scheme for Recapturing Waste Energy in Water Pressure Reducing Valves" IEEE Power and Energy Society General Meeting (PESGM), pp. 1-5, Portland, OR, Aug 2018.

M. Mirmozaffari, A. Alinezhad, and A. Gilanpour, Data Mining Apriori Algorithm for Heart Disease Prediction. Int'l Journal of Computing, Communications & Instrumentation Engg, 4(1), pp.20-23, 2017.

M. Mirmozaffari, A. Alinezhad, and A. Gilanpour, Data Mining Classification Algorithms for Heart Disease Prediction. Int'l Journal of Computing, Communications & Instrumentation Engg, 4(1), pp.11-15, 2017.

M. Mirmozaffari, A. Alinezhad, and A. Gilanpour, Heart disease prediction with data mining clustering algorithms. Int'l Journal of Computing, Communications & Instrumentation Engg, 4(1), pp.16-19, 2017.

M. Mirmozaffari, and A. Alinezhad, Window analysis using two-stage DEA in heart hospitals, October 2017.

N. Sharma, A. Bajpai, and R. Litoriya, Comparison the various clustering algorithms of WEKA tools 2, 73–80, 2012.


Download data is not yet available.


How to Cite
Mirmozaffari, M., Boskabadi, A., Azeem, G., Massah, R., Boskabadi, E., Dolatsara, H. and Liravian, A. 2020. Machine learning Clustering Algorithms Based on the DEA Optimization Approach for Banking System in Developing Countries. European Journal of Engineering Research and Science. 5, 6 (Jun. 2020), 651-658. DOI: