A Tensor-based Method for Credit Scoring
The learning of Credit Scoring has recently gained much attention,and many methods based on machine learning approaches have been proposed.Based on the above research,most of existing Credit Scoring methods take vectors as their input data,and then a function is learned in such a vector space for classification,clustering,or dimensionality reduction.However,in some cases,there are some reasons to take tensors as their input data,e.g.,an image can be considered as a second order tensor.It is reasonable to consider that pixels close to each other are correlated to some extent.In this paper,we represent the data points by second order tensors rather than vectors,and then establish a new Credit Scoring model,which based on support tensor machine.To solve this model,an iterative algorithm is used.This tensor-based algorithm requires a smaller set of decision variables as compared to vector-based approaches,thus,it is helpful to overcome small-sample-size problems in vector-based learning.We compare our proposed method with SVM and LS-SVM on German credit Dataset.Experimental results show the effectiveness of our method.
Credit Scoring support vector machine Support tensor machine Tensor learning
Rui-Ting ZHANG
Canvard College,Beijing Technology and Business University,Beijing 101118,China
国内会议
杭州
英文
1-7
2014-10-18（万方平台首次上网日期，不代表论文的发表时间）