计算机应用与软件2018，Vol.35Issue(4)：293-295,309,4.DOI:10.3969/j.issn.1000-386x.2018.04.054
AUCRF算法在信用风险评价中的特征选择研究
RESEARCH ON THE FEATURE SELECTION OF AUCRF IN CREDIT RISK ASSESSMENT
摘要
Abstract
At present,the methods of feature selection based on random forest algorithm mostly aim at optimizing the overall classification accuracy.However,unequal misclassification cost of imbalance data is widespread in the credit risk assessment process.At this moment,it is unsuitable to use the precision to make the classification performance evaluation index.The AUC value of area under the ROC curve was used as the classification performance index of the binary classification algorithm to construct a feature selection algorithm AUCRF based on the random forest algorithm.The empirical analysis of the Australian credit data in the UCI machine learning database was carried out.The results showed that the model based on AUCRF algorithm obtained higher classification performance with smaller feature subset,AUC =0.934 6.Therefore,the AUCRF algorithm can be used in the credit risk feature selection with the unequal misclassification cost.关键词
AUC值/特征选择/随机森林/信用风险评价Key words
AUC value/Feature selection/Random forest/Credit risk assessment分类
信息技术与安全科学引用本文复制引用
刘忻梅,唐俊,段翀..AUCRF算法在信用风险评价中的特征选择研究[J].计算机应用与软件,2018,35(4):293-295,309,4.基金项目
内蒙古科技大学青年创新基金项目(2012NCL026). （2012NCL026）
