Earticle

초록

영어

As the reform and opening up going into depth over the past three decades and more, the market economic system has been gradually established. The banking industry grows steadily in the process of the reform. It supports economic development, reduces and defends many financial risks in the processof the reform. However, there are many kinds of risks inside of banks, one of which is that the non-performing loans (NPLs) ratio is too high. Therefore, people should focus on how to accurately classify the banking loans into performing and non-performing ones and how to control and prevent the resulting crisis. This paper deeply analyses China’s NPLs problem for the current period, recognizes and classifies loans types by adopting decision trees, Naïve Bayes and support vector machine (SVM) methods. The experiment result found that the decision trees method can well identify the performing loans and non-performing loans; its accuracy is as high as 94%.

목차

Abstract
 1. Introduction
 2. Data and Methodology
  2.1. Data and Data Preprocessing
  2.2. Decision Tree
  2.3. Naïve Bayes
  2.4. Support Vector Machine
 3. Results of Experiment
  3.1. Results of Decision Tree Classification Model
  3.2. Results of Naïve Bayes Classification Model
  3.3. Results of SVM Classification Model
 4. Discussion and Conclusions
 References

키워드

저자

• Zhang Yu [ School of Management, Harbin Institute of Technology, P.R. China/ School of Economics, Harbin University of Science and Technology, P.R. China ]
• Guan Yongsheng [ School of Management, Harbin Institute of Technology, P.R. China / LongJiang Bank, Province Heilongjiang, P.R. China ]
• Yu Gang [ School of Management, Harbin Institute of Technology, P.R. China ]
• Lu Haixia [ School of Management, Harbin Institute of Technology, P.R. China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605