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In this study, we propose an intelligent customer relationship management (CRM) system that uses support vector machine (SVM) ensembles to help enterprise managers effectively manage customer relationship from a risk avoidance perspective. Different from the classical CRM for retaining and targeting profitable customers, the main focus of our proposed CRM system is to identify high-risk customers...
In this study, we propose a least squares bilateral-weighted fuzzy support vector machine (LS-BFSVM) method to evaluate the credit risk problem. The method can not only reduce the computational complexity by considering equality constraints instead of inequalities for the classification problem with a formulation in least squares sense, but also increase the training algorithm's generalization ability...
In this study, a novel hybrid intelligent mining system integrating rough sets theory and support vector machines is developed to extract efficiently association rules from original information table for credit risk evaluation and analysis. In the proposed hybrid intelligent system, support vector machines are used as a tool to extract typical features and filter its noise, which are different from...
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