Gradient Learning Approach for Variable Selection in Credit Scoring
The number of variables used for credit scoring can be quite large, and selecting the most relevant variables becomes an important topic. In this paper, we use gradient learning method for variable selection in credit scoring. The original method in the literature does not work on credit datasets because of the large sample size. To conquer this, we modify the algorithm by resampling data and voting effective variables. Compared with traditional variable selection methods, our approach can handle nonlinear models.
Indez Terms-Credit Scoring Nonlinear Variable Selection Gradient Learning
Quan-Wu Xiao Lei Shi
Joint Advanced Research Center of University of Science and Technology of China and City University of Hong Kong, Suzhou, China
国际会议
北京
英文
219-222
2009-07-24（万方平台首次上网日期，不代表论文的发表时间）