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Credit scoring model development became a very important issue as the credit industry has many competitions. Therefore, most credit scoring models have been widely studied in the areas of statistics to improve the accuracy of credit scoring models during the past few years. This study used three strategies to construct the hybrid FSVM-based credit scoring models to evaluate the applicant's credit...
As the credit industry has been growing rapidly, huge number of consumers' credit data are collected by the credit department of the bank and credit scoring has become a very important issue. Usually, a large amount of redundant information and features are involved in the credit dataset, which leads to lower accuracy and higher complexity of the credit scoring model, so, effective feature selection...
In this study, we applied ensemble machine learning to evaluate credit scoring. With decision tree as the baseline algorithm, two popular ensemble learning methods, bagging and boosting, were evaluated across different experiment conditions: using all 14 features, using selected 6 features on Australian credit data form UCI data set. Results showed that in experiments with all features improved performance...
This paper performs systematic comparative studies on weighted methods including weight C4.5, weighted SVM and weighted rough set with traditional C4.5, SVM and rough set for credit scoring. The experiments show that the weighted methods outperform to the traditional methods when the methods are sensitive to the class distribution.
As the credit industry has been growing rapidly, huge number of consumerspsila credit data are collected by the credit department of the bank and credit scoring has become a very important issue. Usually, a large amount of redundant information and features are involved in the credit dataset, which leads to lower accuracy and higher complexity of the credit scoring model, so, effective feature selection...
As the credit industry has been growing rapidly, huge number of consumerspsila credit data are collected by the credit department of the bank and credit scoring has become a very important issue. Usually, a large amount of redundant information and features are involved in the credit dataset, which leads to lower accuracy and higher complexity of the credit scoring model, so, effective feature selection...
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