원문정보
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
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