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基于Stacking算法实现信贷不平衡数据分类
Classification of Credit Imbalance Data Based on Stacking Algorithm

DOI: 10.12677/HJDM.2020.104027, PP. 254-260

Keywords: 样本不平衡数据,集成学习,Stacking
Sample Unbalanced Data
, Integration Learning, Stacking

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Abstract:

随着大数据技术在应用层面的日渐普及,机器学习、深度学习相关算法在金融风控行业的应用得到了积极的探索。本文基于开源的信用卡数据(该数据具有样本比例极度不平衡的特点),比较不同采样方法对类别不平衡数据分类结果的影响,并应用集成学习算法Stacking融合多个基分类器训练数据,得到更为稳健的分类模型,有效避免了过拟合现象的发生。
With the increasing popularity of big data technology at the application level, the application of machine learning and deep learning related algorithms in the financial risk control industry has been actively explored. Based on open source credit card data (the data has the characteristics of extremely unbalanced sample ratios), this paper compares the impact of different sampling meth-ods on the classification effect of different classification algorithms in the binary classification prob-lem of unbalanced data, and applies ensemble learning algorithm to fuse multiple base classifier training data. A more robust classification model is obtained, effectively avoiding the occurrence of overfitting.

References

[1]  徐永华. 基于支持向量机的信用卡欺诈检测[J]. 计算机仿真, 2011, 28(8): 376-379.
[2]  李赛虎, 张丽娟. 基于特征工程的信用卡欺诈检测策略研究[J]. 现代电子技术, 2019, 42(15): 175-180.
[3]  陈冠宇. 基于kNN-Smote-LSTM的信用卡欺诈风险检测网络模型[D]: [硕士学位论文]. 杭州: 浙江工商大学, 2018.
[4]  Brause, R.W., Langsdorf, T.S. and Hepp, H.M. (1999) Credit Card Fraud Detection by Adaptive Neural Data Mining. Universit?tsbibliothek Frankfurt am Main, Frankfurt am Main.
[5]  Kokkinaki, A.I. (1997) On Atypical Database Transactions: Identification of Probable Frauds Using Machine Learning for User Profiling. IEEE Knowledge and Data Engineering Exchange Workshop, Proceedings, Newport Beach, CA, 4 November 1997, 107-113.
https://doi.org/10.1109/KDEX.1997.629848
[6]  Maes, S., Tuyls, K., Vanschoenwinkel, B., et al. (2002) Credit Card Fraud Detection Using Bayesian and Neural Networks. Proceedings of the 1st International NAISO Congress on Neuro Fuzzy Technologies, Havana, 16-19 January 2002, 261-270.
[7]  Batista, G.E., Bazzan, A.L. and Monard, M.C. (2003) Balancing Training Data for Automated Annotation of Keywords: A Case Study. Conference: II Brazilian Work-shop on Bioinformatics, Macaé, 3-5 December 2003, 10-18.

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