基于改进SMOTE的小额贷款公司客户信用风险非均衡SVM分类  

Imbalanced Data Classification on Micro-Credit Company Customer Credit Risk Assessment Using Improved SMOTE Support Vector Machine

作　　者：衣柏衡 朱建军[1] 李杰[1] 

机构地区：[1]南京航空航天大学经济与管理学院,江苏南京211106

出　　处：《中国管理科学》2016年第3期24-30,共7页Chinese Journal of Management Science

基　　金：国家社会科学基金重点项目(14AZD049);国家自然科学基金资助项目(71171112;71401064);中央高校基本科研业务费专项资金资助(NS2014086);广义虚拟经济研究专项(GX2013-1017(M))

摘　　要：研究了小额贷款公司对客户进行信用风险评估时面临的问题,构建了信用风险评估指标体系,改进了支持向量机(Support Vector Machine,SVM)对非均衡样本分类时分类超平面偏移的不足。首先分析小额贷款公司业务区域性强、信用数据来源不规范、评价标准不一致等特点,给出用于客户信用风险评估的四个维度指标。针对传统SMOTE算法在处理非均衡数据时对全部少数类样本操作的问题,提出仅对错分样本人工合成的改进思想,给出具体算法步骤。将改进算法用于某小额贷款公司客户信用风险评估案例中,分类精确度较其他算法有所提升,表明该方法的可行性和有效性。A great number of machine learning methods have been successfully applied for customer credit risk assessment cases,and support vector machine（SVM）is considered as an ＂off-the-shelf＂supervised learning algorithm to solve classification problem by many researchers.Unfortunately,SVM fails to provide excellent enough classification performance when the data set is imbalanced,i.e.,the accuracy of the majority class is usually much higher than that of the minority class due to the shifting of the hyper-plane.In most cases,people pay more attention on the minority class such as fault diagnosis and credit default.Thus,a Synthetic Minority Over-sampling Technique（SMOTE）is presented to deal with the imbalanced classification by generating new samples in the whole minority class.However,in the process of solving SVM by Sequential Minimal Optimization（SMO）algorithm,only those support vector samples xiwith the correspondingαi 0can affect the position of the hyper-plane while the samples far from the hyper-plane have no influence on the final result.It is obvious that the classic SMOTE algorithm can generate more redundant samples which are far from the hyper-plane.In this article,an improved method for classic SMOTE algorithm is proposed that SMOTE is looped and only misclassified samples in the previous loop are selected to be processed in the next loop until the minority class outnumbers the majority class or all minority class samples are correctly classified.In the empirical study,a data set granted by a micro-credit company in Jiangsu Province is studied.The data set originates from a company that provides loans to local individuals and enterprises for the house condition improving,farm production expanding,business operating and so on.The customers＇ information are analyzed according to the characteristics of micro-loan industry,and a credit risk assessment index system is suggested from four aspects with sixteen attributes in this paper.G-mean and F-measure score are used to evaluate the classificat

关 键 词：小额贷款 信用风险 支持向量机 非均衡数据 SMOTE 

分 类 号：F830.5[经济管理—金融学]