Study of personal credit evaluation under C2C environment based on support vector machines ensemble

With the rapid developing of the Internet, more and more business websites are appearing all around the world. As the proportion of C2C business mode increasing, the problem about personal credit evaluation in the business websites also becomes more critical. The deal methods are very different between the C2C mode and the traditional enterprises in evaluating the credit rank due to the feature of customers in the business websites. In this paper, we construct a support vector machines (SVMs) ensemble method based on fuzzy integral to evaluate personal credit under the environment of electronic commerce. This method aggregates the outputs of separate component SVMs with importance of each component SVM. By comparing the experimental result SVMs ensemble with the single SVM, the neural network ensemble, the proposed method outperforms the single SVM, and neural network ensemble in terms of classification accuracy. It proves that the proposed method is stable, highly accurate, strong robust and feasible. It is useful for providing a sound credit assessment system.