改进的随机平衡采样Bagging算法的网络贷款研究

doi:10.3969/j.issn.1006-2475.2019.04.003

计算机与现代化 ›› 2019, Vol. 0 ›› Issue (04): 11-.doi: 10.3969/j.issn.1006-2475.2019.04.003

改进的随机平衡采样Bagging算法的网络贷款研究

（华南理工大学数学学院，广东广州510641）

收稿日期:2018-12-29 出版日期:2019-04-26 发布日期:2019-04-30
作者简介:郭冰楠（1992-），女，河南三门峡人，硕士研究生，研究方向：数据挖掘，机器学习，E-mail: bingnan_nn@163.com；吴广潮（1972-），男，广东汕头人，副教授，硕士生导师，研究方向：数据挖掘，机器学习。

Improved Random Balanced Sampling Bagging Algorithm for Network Loan Research

(School of Mathematics, South China University of Technology, Guangzhou 510641, China)

Received:2018-12-29 Online:2019-04-26 Published:2019-04-30

摘要/Abstract

摘要： 互联网金融中的网络贷款用户数据具有类别不平衡的特性，严重影响传统分类器的性能。随机平衡采样算法在对原始数据集进行重采样的过程中，将所有样本同等考虑，本文在平衡采样的过程中充分考虑样本点的性能，将其分为3类样本：安全的、边界的、噪声的，针对不同类型的样本采用相应的采样方法，得到平衡的新数据集，然后对该数据集进行Bagging集成，提高算法的泛化性能，结果表明本文改进的随机平衡采样(Improved Random Balanced Sampling， IRBS)Bagging算法可以较好地对网络贷款用户进行分类。

关键词: 类别不平衡, 随机平衡采样, Bagging集成

Abstract: The data of network loan users in Internet finance has the characteristics of class imbalance, which seriously affects the performance of traditional classifiers. The random balanced sampling algorithm considers all samples equally in the process of resampling the original data set. In this paper, the performance of the sample points is fully considered in the process of balanced sampling, and it is divided into three types of samples: safe, boundary, and noisy. The corresponding sampling method is adopted for different types of samples to obtain a balanced new data set, and then the Bagging integration of the data set is performed to improve the generalization performance of the algorithm. The results show that the Improved Random Balanced Sampling(IRBS) Bagging algorithm in this paper can better classify loan users.

Key words: category imbalance, random balanced sampling, Bagging integration

中图分类号:

TP391

郭冰楠，吴广潮. 改进的随机平衡采样Bagging算法的网络贷款研究[J]. 计算机与现代化, 2019, 0(04): 11-.

GUO Bing-nan， WU Guang-chao . Improved Random Balanced Sampling Bagging Algorithm for Network Loan Research[J]. Computer and Modernization, 2019, 0(04): 11-.

参考文献

［1］ GUO Y H, ZHOU W J, LUO C Y, et al. Instance-based credit risk assessment for investment decisions in P2P lending［J］. European Journal of Operational Research, 2016,249(2):417-426.
［2］李毅,姜天英,刘亚茹. 基于不平衡样本的互联网个人信用评估研究［J］. 统计与信息论坛, 2017,32(2):84-90.
［3］ PHUA C, ALAHAKOON D, LEE V. Minority report in fraud detection:Classification of skewed data［J］. ACM SIGKDD Explorations Newsletter, 2004,6(1):50-59.
［4］ CHAWLA N V， JAPKOWICZ N， KOTCZ A. Editorial:Special issue on learning from imblanced datasets［J］. ACM SIGKDD Explorations Newsletter， 2004,6(1):1-6.
［5］尹超. 基于网络信贷数据的分类器构造［D］. 合肥:中国科学技术大学， 2016.
［6］ BATISTA G E A P, PRATI R C, MONARD M C. A study of the behavior of several methods for balancing machine learning training data［J］. ACM SIGKDD Explorations Newsletter, 2004,6(1):20-29.
［7］李军. 不平衡数据学习的研究［D］. 长春:吉林大学， 2011.
［8］李勇,刘战东,张海军. 不平衡数据的集成分类算法综述［J］. 计算机应用研究, 2014,31(5):1287-1291.
［9］季梦遥,袁磊. 不平衡数据的随机平衡采样bagging算法分类研究［J］. 贵州大学学报(自然科学版), 2017,34(6):54-58.
［10］娄康,薛彦兵,张桦,等. 基于代价敏感深度决策树的公交车环境人脸检测［J］. 计算机应用, 2017,37(11):3152-3156.
［11］DEVI D, BISWAS S K, PURKAYASTHA B. Redundancy-driven modified Tomek-link based undersampling: A solution to class imbalance［J］. Pattern Recognition Letters, 2017,93(1):3-12.
［12］周志华. 机器学习［M］. 北京:清华大学出版社, 2016:171-196.
［13］BARUA S, ISLAM M M, MURASE K. A novel synthetic minority oversampling technique for imbalances data set learning［C］// Proceedings of the 2011 International Conference on Neural Information Processing. 2011：735-744.
［14］BUNKHUMPORNPAT C, SINAPIROMSARAN K, LURSINSAP C. Safe-Level-SMOTE： Safe-level-synthetic minority over-sampling technique for handing the class imbalanced problem［C］// Pacific-Asia Conference on Knowledge Discovery and Data Mining(PAKDD). 2009:475-482.
［15］于化龙. 类别不平衡学习:理论与算法［M］. 北京:清华大学出版社, 2017:12-20.
［16］MADAN N. Providing banking loan to customers based on J48 classifier algorithm combined with neural networks［J］. IJLTEMAS， 2017,3(6):58-62.
［17］KRICHENE A. Using a naive Bayesian classifier methodology for loan risk assessment: Evidence from a Tunisian commercial bank［J］. Journal of Economics， Finance and Administrative Science, 2017,22(42):3-24.
［18］余华银,雷雅慧. 基于决策树与Logistic回归的P2P网贷平台信用风险评价比较分析［J］. 长春大学学报, 2017,27(9):13-16.
［19］HU S G, LIANG Y F, MA L T, et al. MSMOTE: Improving classification performance when training data is imbalanced［C］// IEEE 2nd International Workshop on Computer Science and Engineering(WCSE’09). 2009,2:13-17.
［20］HANSEN L K, SALAMON P. Neural network ensembles［J］. IEEE Transactions on Pattern Analysis & Machine Intelligence, 1990,12(10):993-1001.
［21］LIU X Y, WU J X, ZHOU Z H. Exploratory undersampling for class-imbalance learning［J］. IEEE Transactions on Systems, Man, and Cybernetics (Part B), 2009,39(2):539-550.
［22］ZHANG Y L, ZHOU J, ZHENG W H, et al. Distributed Deep Forest and Its Application to Automatic Detection of Cash-out Fraud［EB/OL］. (2018-05-27). https://arxiv.org/abs/1805.04234?context=cs.LG.

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改进的随机平衡采样Bagging算法的网络贷款研究

Improved Random Balanced Sampling Bagging Algorithm for Network Loan Research

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