Comparative analysis of data mining methods for bankruptcy prediction |
Abstract:
A great deal of research has been devoted to prediction of bankruptcy, to include application of data mining.Neural networks, support vector machines, and other algorithms often fit data well, but because of lack ofcomprehensibility, they are considered black box technologies. Conversely, decision trees are more comprehensibleby human users. However, sometimes far too many rules result in another form of incomprehensibility.The number of rules obtained from decision tree algorithms can be controlled to some degree throughsetting different minimum support levels. This study applies a variety of data mining tools to bankruptcydata, with the purpose of comparing accuracy and number of rules. For this data, decision trees were foundto be relatively more accurate compared to neural networks and support vector machines, but there were more rule nodes than desired. Adjustment of minimum support yielded more tractable rule sets. |
| Keywords: |
Bankruptcy prediction Data mining Neural networks Decision trees Support vector machines Transparency Transportability |
| Author(s): |
David L. Olson , Dursun Delen , Yanyan Meng |
| Source: |
Decision Support Systems 52 (2012) 464–473 |
| Subject: |
مدیریت مالی |
| Category: |
مقاله مجله |
| Release Date: |
2012 |
| No of Pages: |
10 |
| Price(Tomans): |
0 |
|
بر اساس شرایط و ضوابط ارسال مقاله در سایت مدیر، این مطلب توسط یکی از نویسندگان ارسال گردیده است. در صورت مشاهده هرگونه تخلف، با تکمیل فرم گزارش تخلف حقوق مؤلفین مراتب را جهت پیگیری اطلاع دهید.
|
|
|