| 融合数据增广技术与机器学习算法的个人信用评分研究 |
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| 引用本文： | 陆健健,江开忠.融合数据增广技术与机器学习算法的个人信用评分研究[J].教育技术导刊,2009,19(8):40-43. |
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| 作者姓名： | 陆健健 江开忠 |
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| 作者单位： | 1. 上海工程技术大学 管理学院,2. 上海工程技术大学 数理与统计学院，上海 201600 |
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| 基金项目： | 上海工程技术大学研究生创新项目（18-01114） |
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| 摘 要： | 为了提高个人信用评分模型算法预测精准率，受视觉领域数据增广思路启发，提出融合数据增广技术与机器学习算法的个人信用评分模型。该模型首先对原始个人信用数据进行数据增广处理，然后基于机器学习分类算法训练一个二分类个人信用评分模型，最后基于公开个人信用数据集，分别建立未经过数据增广和经过数据增广处理后的个人信用评分模型。对比准确率、精确率、召回率、F1 得分、AUC 值和 ROC 曲线等 6 个性能评价指标，结果显示，相较于仅基于机器学习算法的个人信用评分模型，融合了数据增广技术与机器学习算法的个人信用评分模型使得分类性能得到了一定提升，分类准确率平均高出 5%。
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| 关 键 词： | 数据增广技术 机器学习算法 个人信用评分 分类性能评价指标 |
| 收稿时间： | 2019-11-12 |
Research on Personal Credit Score of Fusion Data Augmentation Technology and Machine Learning Algorithm |
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| LU Jian-jian,JIANG Kai-zhong.Research on Personal Credit Score of Fusion Data Augmentation Technology and Machine Learning Algorithm[J].Introduction of Educational Technology,2009,19(8):40-43. |
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| Authors: | LU Jian-jian JIANG Kai-zhong |
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| Institution: | 1. School of Management，Shanghai University of Engineering Science| 2. College of Mathematics and Statistics，Shanghai University of Engineering Science，Shanghai 201600，China |
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| Abstract: | Inspired by data augmentation in computer vision，it is feasible to increase the number of training data and make the data set as diverse as possible so as to improve the accuracy of the model of personal credit scoring. After the data is augmented，the performance of the classification task can often be greatly improved. This paper firstly proposes a personal credit scoring model based on data augmentation algorithm. Based on the data augmentation of original personal credit data，a personal credit model is established based on supervised machine learning algorithm. In the empirical part，this paper builds a personal credit scoring model that has not undergone data augmentation and data augmentation processing based on public personal credit data sets. Six performance evaluation indicators，such as accuracy，accuracy，recall，F1 score，AUC value and ROC curve showed that the classification performance was improved more than 5% by the personal credit scoring model based on data augmentation technology. |
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| Keywords: | data augmentation machine learning credit scoring classification performance evaluation metrics |
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