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

نوع مقاله: مقاله پژوهشی

نویسندگان

1 تهران، دانشگاه تربیت مدرس، گروه آموزشی مدیریت

2 کارشناس ارشد مهندسی نرم افزار گرایش هوش مصنوعی

3 گروه مدیریت بانکی,دانشکده علوم اقتصادی، موسسه علوم بانکی ,تهران, ایران

4 گروه مدیریت بازرگانی، دانشکده مدیریت، واحد سنندج، دانشگاه آزاد اسلامی، سنندج، ایران

5 گروه مدیریت دولتی,دانشکده مدیریت، واحدمهاباد،دانشگاه آزاد اسلامی ,مهاباد, ایران

چکیده

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.

کلیدواژه‌ها


عنوان مقاله [English]

Explaining the categories of support vector machine and neural network for Ranking of bank branches

نویسندگان [English]

  • 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.
چکیده [English]

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.

کلیدواژه‌ها [English]

  • Data mining
  • Banking
  • Support vector machine
  • Neural network
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