Abstract. Credit scoring is a very typical classification problem in Data Mining. Many classification methods have been presented in the literatures to tackle this problem. The decision tree method is a particularly effective method to build a classifier from the sample data. Decision tree classification method has higher prediction accuracy for the problems of classification, and can automatically generate classification rules. However, the original sample data sets used to gen-erate the decision tree classification model often contain many noise or redundant data. These data will have a great impact on the prediction accuracy of the clas-sifier. Therefore, it is necessary and very important to preprocess the original sample data. On this ...
The enormous growth experienced by the credit industry has led researchers to develop sophisticated ...
Generation of an Integrated Model is an important technique in the research area. It is a powerful t...
The enormous growth experienced by the credit industry has led researchers to develop sophisticated ...
Credit scoring is the term used to describe methods utilised for classifying applicants for credit i...
In the context of credit scoring, ensemble methods based on decision trees, such as the random fores...
The performance evaluation is a significant problem nowadays. Whether it is an evaluation inside the...
In the context of credit scoring, ensemble methods based on decision trees, such as the random fores...
In the context of credit scoring, ensemble methods based on decision trees, such as the random fores...
In the context of credit scoring, ensemble methods based on decision trees, such as the random fores...
In the context of credit scoring, ensemble methods based on decision trees, such as the random fores...
This study is focused on enhancing Decision Tree on its capabilities in classification as well as pr...
This study is focused on enhancing Decision Tree on its capabilities in classification as well as pr...
International audienceThe application of classification models in the credit rating of banking custo...
For many years lenders have been using traditional statistical techniques such as logistic regressio...
Credit scoring is the term used by the credit industry to describe methods used for classifying appl...
The enormous growth experienced by the credit industry has led researchers to develop sophisticated ...
Generation of an Integrated Model is an important technique in the research area. It is a powerful t...
The enormous growth experienced by the credit industry has led researchers to develop sophisticated ...
Credit scoring is the term used to describe methods utilised for classifying applicants for credit i...
In the context of credit scoring, ensemble methods based on decision trees, such as the random fores...
The performance evaluation is a significant problem nowadays. Whether it is an evaluation inside the...
In the context of credit scoring, ensemble methods based on decision trees, such as the random fores...
In the context of credit scoring, ensemble methods based on decision trees, such as the random fores...
In the context of credit scoring, ensemble methods based on decision trees, such as the random fores...
In the context of credit scoring, ensemble methods based on decision trees, such as the random fores...
This study is focused on enhancing Decision Tree on its capabilities in classification as well as pr...
This study is focused on enhancing Decision Tree on its capabilities in classification as well as pr...
International audienceThe application of classification models in the credit rating of banking custo...
For many years lenders have been using traditional statistical techniques such as logistic regressio...
Credit scoring is the term used by the credit industry to describe methods used for classifying appl...
The enormous growth experienced by the credit industry has led researchers to develop sophisticated ...
Generation of an Integrated Model is an important technique in the research area. It is a powerful t...
The enormous growth experienced by the credit industry has led researchers to develop sophisticated ...