Evaluation Of Classification Models And Improvement Of SMOTEBagging Model Based On MCDM In The Case Of P2P Personal Credit Risk Prediction

The development of Internet finance and the acceleration of the commercialization of personal credit not only enable people to enjoy more and more convenient services with personal credit,but also provide a new way of thinking for the country to improve the credit system.For the enterprises that provide these service,effective prediction of the user's personal credit risk and find the potential default user,is the foundation to improve the level of risk management and ensure the quality of service.Therefore,research on personal credit risk prediction model has important value and significance.The traditional model has been unable to meet the current demand for risk management while the classification model represented by the data mining technology has become a mainstream technology to construct the personal credit risk prediction model.With different classification models,how to select the best classification model for personal credit risk prediction in their own data sets have become the concerns of the enterprises.Based on the MCDM method,this paper mainly studies two problems,one is how to evaluate and select the prediction model under a single dataset,and the other is how to improve the ability of the prediction model to identify the potential default customers.To solve the first problem,and considering the feature space can affect the performance of the model,this paper proposed a multi spatial multi criteria evaluation framework which uses a variety of feature selection methods and combined with the MCDM method.Using the proposed evaluation framework and the data from the famous American P2 P loan platform Prosper.com,5 individual classification models are comprehensive evaluated over 6 criteria by TOPSIS and compared with each other,as a demonstration of feature space construction and classification model selection for companies.The experimental results show that the experiment results show that the BPNN,LR and SVM have a good comprehensive performance in the personal credit risk prediction of Prosper.com.In order to further improve the prediction accuracy for default users(TPR),the three classification models are used as the base classifier and SMOTEBagging model is used for ensemble learning.Personal credit risk prediction has imbalanced dataset problem.In such a situation,the SMOTEBagging model has better TPR performance than the traditional Bagging model.In order to get a higher TPR without sacrifice the overall performance,we.improve the SMOTEBagging model base on AHP method,and construct a model name AHP-Based Bagging.We first check the effectiveness of AHP-Based Bagging model under 27 imbalanced datasets and find that under the confidence level of 0.05,the AHP-Based Bagging model can achieve a significantly higher TPR with just half the ensemble size of the SMOTEBagging model,and the performance in AUC and F1-Measure showed no significantly worse.Then,the AHP-Based Bagging model is applied to the personal credit risk prediction of Prosper.com,and gets a better comprehensive performance than SMOTEBagging.In addition,the prediction accuracy of the default users is also further improved.