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Performance of artificial neural network (ANN), one of the useful tools used for credit scoring models, is increased by proposed methodology in present study. Whereas reducing the rate of error, in order to obtaining the best possible result, and optimal network of ANN are very important, in this paper, for reducing the errors of the artificial neural networks, voting algorithm will be offered. Using...
Bankruptcy prediction is a hot topic. Traditional methods consist of univariate model and multivariate model such as neural network. However, the NNs can not extract effective rules. Thus, a novel approach was proposed in this paper to extract rules. First, t-test method was used to select 5 features from 55 original features. Second, the rule encoding was constructed. Third, the ant colony algorithm...
Time series has been widely applied in the real world; traditional methods can hardly solve the dynamic environment issue resulting from the assumption of stationary process. Many traditional models and artificial intelligence technologies had been developed under this assumption, and adapted the dynamic environment based on the time-varying characteristic. But these models still has drawback of dividing...
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 constructs a hybrid SVM-based credit scoring models to evaluate the applicantpsilas credit score from the applicantpsilas...
Credit scoring models are very important tools for credit granting institutions to assess the credit risk of their customers. Most previous researches focus on improving predictive accuracy of models. In this research, a weighted LS-SVM credit scoring model with Area under ROC curve maximization is proposed and optimized by direct search. The tests on two real-world datasets show that it is effective...
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