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  • تبیین دسته بندهای ماشین بردار پشتیبان و شبکه عصبی جهت درجه بندی شعب بانک

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آدرس مقاله


کد مقاله : IMJ-2006-1396 (R2) بازدید : 159 صفحه: 53 - 70

نوع مقاله: پژوهشی

تبیین دسته بندهای ماشین بردار پشتیبان و شبکه عصبی جهت درجه بندی شعب بانک

محورهای موضوعی : مدیریت صنعتی

davod khosroanjom 1 , mohamamd elyasi 2 , behzad keshanchi 3 , Bahare Boobanian 4 , shovana abdollahi 5

1 - Department of Management, College of Management, & Economic, PhD Student at Tarbiat Modres. Tehran. Iran
2 - master of soft ware engenering
3 - Department of Banking Management, College of Economic Sciences, School of Banking Sciences, Tehran, Iran
4 - Department of Business Management, College of Management, Sannadaj Branch, Islamic azad university Sannadaj , Iran.
5 - Department of Public Management, College of Management, Mahabad Branch, Islamic azad university Mahabad, Iran.

تاریخ دریافت : 1399/03/15 تاریخ پذیرش : 1399/08/17 تاریخ انتشار : 1399/08/19

کلید واژه: ماشین بردار پشتیبان, داده کاوی, بانکداری, شبکه عصبی,

چکیده مقاله :

در صنعت بانکداری اطلاعات زیادی وجود دارد که شناسایی آن از اهمیت ویژه‌ای برخوردار است. استفاده از تکنیک های داده کاوی نه تنها کیفیت را بهبود می بخشد بلکه منجر به مزایای رقابتی و ارتقای جایگاه بازار نیز می گردد. با استفاده از داده کاوی و به منظور تجزیه و تحلیل الگوها و روندها، بانکها می توانند صحت این را پیش بینی کنند درجه بندی شعب بانک چگونه است. در این مقاله درجه بندی شعب یکی از بانکهای بزرگ تجاری (تعداد شعب انتخاب شده 1825 شعبه و تعداد ویژگی های مورد استفاده 57 ویژگی) با استفاده از دسته بندهای ماشین بردار پشتیبان و شبکه عصبی چند لایه پرسپترون برروی داده های واقعی انجام گرفت. نتایج ارزیابی مربوط به ماشین بردار پشتیبان نشان داد که این دسته بند دارای کارایی پایین تری برای روش پیشنهادی است. اما استفاده از شبکه های عصبی و ترکیب آن با PCA نشان داد که دارای معیارهای کارایی بالایی است. مقادیر مربوط به کارایی و صحت با استفاده از شبکه عصبی با دقت بسیار بالایی بدست آمد.

چکیده انگلیسی:

There is a lot of information in the banking industry that is of particular importance in identifying it. The use of data mining techniques not only improves quality but also leads to competitive advantages and market positioning. By using data mining and in order to analyze patterns and trends, banks can predict the accuracy of how bank branches are ranked. In this paper, the branches of one of the large commercial banks (number of selected branches 1825 branches and the number of features used 57 features) were performed on real data using support vector machine categories and multi layer perceptron neural network. The evaluation results related to the support vector machine showed that this classifier has lower efficiency for the proposed method. However, the use of neural networks and its combination with PCA showed that it has high performance criteria. Values related to efficiency and accuracy were obtained using neural network with very high accuracy.

منابع و مأخذ:


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_||_

  1. Agany, D., Pietri, J., and  Gnimpieba, E. (2020). Assessment of vector-host-pathogen relationships using data mining and machine learning. Computational and Structural Biotechnology Journal, 18 (1), 1704-1721.
    2.
  2. Azar, A., Ahmadi, P., & Sabt, M.V. (2010). Model Design for Personnel Selection with Data Mining Approach (Case Study: A Commerce Bank of Iran).Journal Of Information Technology Management, 2(4), 3-22 (In Farsi). 
    3.
  3. Bahari, F., and Elayidom, S.(2015). An Efficient CRM-Data Mining Framework for the Prediction of Customer Behaviour. International Conference on Information and Communication Technologies. Procedia Computer Science. 46(1),725 – 731
    4.
  4. Barchman,R.J., Khabaza,T., Kloesgen,W., Piatetsky-Shapiro,G., and Simoudis,E. (1996). Mining Business Databases. Communications of the ACM.39 (11).
    5.
  5. Berry,M.J.A. and Linoff,G.S.(2002).Mastering Data Mining. John Wiley & Sons, New York.
    6.
  6. Bradley,P.S.,Fayyad.U.M., and Mangasarian,O.L.(1999). Mathematical Programming for Data Mining: Formulation and Challenges. Journal on Computing, 11(3),217-238.
    7.
  7. Cabena,P., Hadjinian,P., Stadler,R., Verhees,J., and Zanasi,A. (1998). Discovering Data Mining: From Concept to Implementation. Prentice Hall. New Jersey.
    8.
  8. Carvajal, M., Viacrusis, K., Hernandez, L., Amalin, D., and Watanabe K. (2018). Machine learning methods reveal the temporal pattern of dengue incidence using meteorological factors in metropolitan Manila, Philippines. BMC. 18(1), 183-196.
    9.
  9. Chen, Y., Kuo, M., Wu, S., Tang, K. (2009). Discovering recency, frequency, and monetary (RFM) sequential patterns from customers’ purchasing data, Electronic Commerce Research and Applications, 8 (5), 241–251.
    10.
  10. Chye,K.H. and Gerry, C.K.L. July-Dec.(2002). Data Mining and Customer Relationship Marketing in the Banking Industry:Singapore. Management Review, 24(2),1-27.
    11.
  11. Coenen, F. (2011). Data mining: past, present and future. The Knowledge Engineering Review. 26(1), 25-29.
    12.
  12. Crook, J.N., Edelman,D.B., and Thomas,L.C. (2001). Editorial Review. Journal of the Operation Research Society. 52(9).972-973.
    13.
  13. Deneke C, Rentzsch R, Renard Y. and Paprbag. (2017). a machine learning approach for the detection of novel pathogens from NGS data. 7(1), 25-38.
    14.
  14. Decker,P. (1998). Data Mining's Hidden Dangers. Banking Strategy.6-14.
    15.
  15. Deshpande, S.P. & Thakare, V.M. (2010). Data Mining System and Applications: A Review. International Journal of Distributed and Parallel systems (IJDPS), 1(1), 32-44.
    16.
  16. Divandari, A., Shabahang, R., Mohammdpoor, M., Moosavi, R. (2007). Designing Portfolio Management System for Banking Loans By Using Data Mining Technology. 6(4), 207-236 (In Persian). 
    17.
  17. El-zehry A.M., El-bakry H.M, and El-ksasy M.S.,(2013). Applying Data Mining Techniques for Customer Relationship Management: A Survey. (IJCSIS) International Journal of Computer Science and Information Security, 11(11), 1-8.
    18.
  18. Fabris, P. 1998. Advanced Navigation.CIO. May 15.
    19.
  19. Fayyad, U. & Piatetsky-Shapiro, G. & Smyth, P. (1997). From Data Mining to Knowledge Discovery in Databases. American Association for Artificial Intelligence (AAAI), 17 (3), 37-54.
    20.
  20. Groth. R. (1998). Data Mining: A Hands-On Approach for Business Professionals. Prentice Hall, New Jersey.
    21.
  21. Hasanzadeh, A., Ghanbari, M.H., & Elahi, S. (2012). Classification Of Mobile Banking Users By Data Mining Approach: Comparison Between Artificial Neural Networks And Naive Bayes Techniques. Management Research In Iran (Modares Human Sciences), 16(2), 57-71 (In Persian).
    22.
  22. Hwang,H., Ku,C., Yen,D.C., and Cheng,C. (2002). Critical Factors Influencing the Adoption of Data Warehouse Technology: A Study of the Banking Industry in Taiwan. Decision Support Systems, 10(39), 1-21.
    23.
  23. Jayachandran, S., Sharma, S., Kaufman, P., Raman, P. (2005). The role of relational information process and technology use in customer relationship management. Journal of Marketing, 69(4), 177–192.
    24.
  24. Karakatsanis, L., AlKhader, W., MacCrory, F., Alibasic, A., Atif, M.O., Aung, Z., and Lee,W.W.(2016). Data Mining Approach to Monitoring the Requirements of the Job Market: A Case Study. Information Systems. 4-21.
    25.
  25. Khajevand, S., Taghavi, M., & Najafi, E. (2012). Customer classification of Bank Saderat Iran using data mining. Journal of Management Studies, 22(179), 67-82 (In Persian). 
    26.
  26. .Khanbabei, M., Movahedi, F., Alborzi, M., & Radfar, R. (2019). Framework of Using Data Mining for Business Process Improvement. Production and Operations Management, 10(1), 25-45 (In Persian). 
    27.
  27. Kim, N. & Atuahene-Gima, K. (2010). Using Exploratory & Exploitative Market Learning for New Product Development. Journal of Product Innovation Management, 27(4), 519-536.
    28.
  28. Kiss, F. (2003). Credit scoring process from a knowledge management prospective. Periodica Polytechnica Ser. SOC. MAN. SCI, 11 (1), 95-110.
    29.
  29. LEE, S. J. & SIAU, K. (2001). A review of data mining techniques. Industrial Management & Data System, 101(1), Pages 41-46.
    30.
  30. Lin, C.C., Chiu, A.A., Huang, Y.S., and Yen, D.C.(2015). Detecting the financial statement fraud: The analysis of the differences between data mining techniques and experts’ judgments. Knowledge-Based Systems, 89(1), 459–470.
    31.
  31. Luo, Y., Zheng, Q., Jayaraman, V. (2010). Managing Business Process Outsourcing. Journal of science Direct, 3(1), 205-217.
    32.
  32. Ltifi, H., Benmohamed,E., Kolski,C., and  Ben Ayed,m.(2016). Enhanced visual data mining process for dynamic decision-making. Knowledge-Based Systems. 112(1), 166–181.
    33.
  33. Marbán, O. & Segovia, J. & Menasalvas, E. & Fernández-Baizán, C. (2009). Toward data mining engineering: a software engineering approach. Information Systems, 34 (1), 87-107.
    34.
  34. Mesforoush, A., Tarokh, M.J. (2013). Customer Profitability Segmentation for SMEs Case Study: Network Equipment Company. Novel science, Research in Industrial Engineering, 2(1), 30-44.
    35.
  35. Pourzarandi, M.E.,  Shabahang, R., and  Lucas, C., (2004).Designing and Developing an Appropriate Inventory Management Model using Data Mining and Knowledge Discovery Technology, The Application of Neural Networks, Economic & Management. Quarterly Journal of the Islamic Azad University, (61), 92-105.
    36.
  36. Radfar, R., Nezafati, N., & Yoosefi, S. (2014).Classification Of Internet Banking Customers Using Data Mining Algorithms. Journal of Information Technology Management, 6(1), 71-90 (In Persian). 
    37.
  37. Rangriez, H., & Bayrami, Z. (2019).The Impact of E-CRM on Customer Loyalty Using Data Mining Techniques. Journal of Business Intelligence Management Studies, 7(27), 175-205 (In Persian). 
    38.
  38. Rygielski C., Wang J. C.,Yen D. C. (2002). Data mining techniques for customer relationship management. Technology in Society, 24(1), 483-502.
    39.
  39. Sepehri M M, norouzi A, Chubdar S, & Teymourpour B. (2015). Developing a model for discovering the causes of customer churn from banking services via hybrid approach of data mining and survey. IQBQ. 15 (4), 97-125 (In Persian). 
    40.
  40. Shmueli G., Patel Nitin R., Bruce Peter C.(2007). Data mining for business intelligence concepts, techniques, and applications";in Microsoft Office Excel with Xlminer, Wiley-Interscience
    41.
  41. Sing’oei, L. and Wang,J. (2013).  Data Mining Framework for Direct Marketing: A Case Study of Bank Marketing. International Journal of Computer Science Issues, 10(2), 1-7
    42.
  42. Thillainayagam V., (2012). Data Mining techniques and applications-A review. I-manager’s Journal on Software Engineering, 6(1), 1-6.
    43.
  43. Torkzadeh, G., Cha-Jan Chang, J., Hansen, G.W. (2006). Identifying issues in customer  relationship management at Merck-Medco. Decision Support Systems, Journal of science Direct, 42 (2), 1116-1130.
    44.
  44. Teymourpour, B. (2015). Applications and challenges of data mining in the banking industry, Fifth Annual Conference on Electronic Banking and Payment Systems (In Persian). 
    45.
  45. Turki M. Alkheliwi. (2011). Enhancing risk prediction in financial application using data mining and game theory principles. THESIS Submitted in partial satisfaction of the requirements for the degree of MASTER OF SCIENCE, Michigan State University.
    46.
  46. Yaning, L. & Kia, L. & Fei, G. (2008). Data mining approach and application in CRM project for internet-focused banking. IEEE, Wireless Communications, Networking and Mobile Computing, 2008. WiCOM '08. 4th International Conference on, 12-14 Oct. 1-4.
    47.
  47. Yoon, Y. (1999). Discovering Knowledge in Corporate Database. Information Systems Management, 16(2), 64-71.
    48.

 

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