A New Hybrid Method for Credit Scoring Based on Clustering and Support Vector Machine (CIsSVM)
the main goal in the credit scoring process is forecasting every customers adequacy in accomplishment of their obligations precisely as much as possible.Although this technique is identical with regular binary classification tasks but it has a few crucial differences.Whereas,based on financial credit rules,a customer is considered based on a degree of goodness or badness,one cannot allocate them to one of two distinct classes.Although,in order to solve this problem,researchers have tried to use classification methods that enable producing a posterior probability of default instead of pure classification results,all of them have drawbacks,which cause many serious problems.In addition,the performance of the final model isnt so high and the error rate is remarkable.In this paper a new hybrid method to address these problems is proposed,which can efficiently increase credit scoring accuracy with no previous assumption.Comparison of the obtained results on proposed hybrid method with Logit model in a real world dataset indicates a significant performance improvement.
Credit scoring Clustering Grid search
Mohammad Fereydon Kiani Fariborz Mahmoudi
Islamic Azad University.Qazv in Branch Qazvin,Iran Islamic Azad University,Qazvin Branch Qazvin,Iran
国际会议
重庆
英文
585-589
2010-09-17（万方平台首次上网日期，不代表论文的发表时间）