The Applications and its Strategies of AdaBoost Algorithm in the Credit Ratings on Feature Selection:Taking Iron and Steel Sector For Example
In this paper,Adaboost algorithm is applied in credit ratings firstly,and empirical analysis shows that Adaboost algorithm on the basis of 18 indexes selected as regression variables fits the credit rates of 39 listed iron and steel companies of China very well.The discrimination errors are 2.56% after 10 iterations,given iterations added then its errors could reach zero and output classification results stably.In additions,the index importance outputs used from two aspects can reselect and refine the nine key indexes among the eighteen indexes.After using Adaboost algorithm to test again,we can find that the nine indexes reduced do not cut down the classification information of the models and the rating corrections do not slide down either.
credit ratings feature selection AdaBoost algorithms iron and steel sector
LI Hui
School of Statistics, Renmin University of China, Beijing, PRC, 100872;School of Science and Information, Qingdao Agricultural University, Qingdao, PRC, 266109
国内会议
青岛
英文
732-741
2012-07-18（万方平台首次上网日期，不代表论文的发表时间）