Earticle

초록

영어

The non-performing loans (NPL) prediction plays an important role in business bank. However, there is still a large gap between the requirement of prediction performance and current techniques. In this paper data mining approaches is used to predict the NPL. Both macroeconomic and bank-specific variables are collected to form the feature set firstly. Based on selected features, the study firstly applies single basic classifiers such as decision tree, k nearest neighbors and support vector machine (SVM) to model the problem of NPL. Bagging and AdaBoost are described in this paper as two different method of multiple classifier fusion, to build prediction models. In this experiment, non-performing loans data with 96 features and 10415 instances of a business bank is collected. F-mean and The Area under the ROC Curve (AUC) are considered as metrics of classification. The results illustrate that multiple classifier fusion algorithms outperform single basic classifier. The model built by multiple classifiers fusion can produce better prediction results. Furthermore, the AdaBoost method performs much better than bagging method in processing NPL.

목차

Abstract
 1. Introduction
 2. Related Works
  2.1. Modeling Non-Performing Loan
  2.2. Features of Non-Performing Loans 
  2.3. Data Mining of NPL
 3. Methodology
  3.1. Basic Classifiers
  3.2. Strategy of Multiple Classifier Fusion
 4. Experiment
  4.1. Dataset
  4.2. Data Pre-processing
  4.3. Results and Analysis
 5. Conclusion
 References

키워드

저자

• Wan Jie [ School of Energy Science and Engineering, Harbin Institute of Technology, P.R.China / Nangjing Qiuya Power Horizon Information Technology Company Limited, P.R.China ]
• Yue Zeng-lei [ Heilongjiang science and Technology Information Research Institute, P.R.China ]
• Yang Dong-hui [ School of Economics and Management, Southeast University, P.R.China ] Corresponding Author
• ZhangYu [ School of Management, Harbin Institute of Technology, P.R.China ]
• Liu Jiao [ School of Energy Science and Engineering, Harbin Institute of Technology, P.R.China ]
• Liu Zhi [ Nangjing Qiuya Power Horizon Information Technology Company Limited, P.R.China ]
• Liu Jinfu [ School of Energy Science and Engineering, Harbin Institute of Technology, P.R.China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605