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基于机器学习分类方法的信用卡审批应用
The Application of Credit Approval Based on Machine Learning Classification Method

DOI: 10.12677/HJDM.2016.63012, PP. 97-105

Keywords: 信用卡申请,机器学习分类,随机森林
Credit Card Application
, Machine Learning Classification, Random Forest

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Abstract:

传统的信用卡审批方法往往是依靠信贷人员的经验进行审批,确定信用卡申请者是否符合申请条件,这种审批方法有很大的随意性和不稳定性。本文利用R软件并将最新的六种机器学习分类方法——决策树分类、Adaboost分类、Bagging分类、随机森林分类、支持向量机分类、人工神经网络引入到信用卡申请管理中,建立了自动化的申请管理体系,有效地降低了审批结果的随意性和不稳定性,并通过八折交叉验证计算出每种方法的分类均方误差并进行对比,筛选出分类效果最好的方法。结果表明:随机森林分类的分类误差是最小的。
The traditional method of credit card approval is often rely on the experience of credit personnel and is to decide whether the credit card applicants meet the conditions of application. Obviously, this approval method has a lot of randomness and instability. In this paper, we take advantages of R software and introduce the six latest machine learning classification method, decision tree clas-sification, AdaBoost, Bagging classification, random forest classifier, support vector machine (SVM) classification, artificial neural network (Ann) into the credit card application management, then establish the automatic application management system, effectively reducing the randomness and instability of the examination and approval results. Finally we calculate the mean square error of all the classification method through 8-fold cross validation and chose the classification with the best effect. The result shows that the classification error of random forest classification is the smallest.

References

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