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A method combined of support vector machine and F-scores for customer classification

To overcome the shortages of the existing customer classification method such as strict hypothesis, poor generalization ability, low prediction accuracy and low learning rate etc., a method combined of F-scores and support vector machine for customer classification was proposed, and was applied to the problem of bank credit card customer classification. Empirical analysis shows the validation accuracies of the final model can achieve 95% or more, which concludes that learning and generalization abilities of this model are excellent.