[1] He H B, Garcia E A. Learning from imbalanced data[J]. IEEE Transactions on Knowledge and Data Engineering, 2009, 21(9): 1263-1284.
[2] Krawczyk B. Learning from imbalanced data: open challenges and future directions[J]. Progress in Artificial Intelligence, 2016, 5(4): 221-232.
[3] Zhang C, Tan K C, Li H, et al. A cost-sensitive deep belief network for imbalanced classification[J]. IEEE transactions on Neural Networks and Learning Systems, 2018, 30(1): 1-14.
[4] García S, Zhang Z L, Altalhi A, et al. Dynamic ensemble selection for multi-class imbalanced datasets[J]. Information Sciences, 2018, 445/446: 22-37.
[5] Tsai C F, Lin W C, Hu Y H, et al. Under-sampling class imbalanced datasets by combining clustering analysis and instance selection[J]. Information Sciences, 2019, 477: 47-54.
[6] Chawla N V, Bowyer K W, Hall L O, et al. SMOTE: synthetic minority over-sampling technique[J]. Journal of Artificial Intelligence Research, 2002, 16(1): 321-357.
[7] He H B, Bai Y, Garcia E A, et al. ADASYN: adaptive synthetic sampling approach for imbalanced learning[C]//Proceedings of the 2008 IEEE International Joint Conference on Neural Networks, Hong Kong, China, Jun 1-8, 2008. Washington: IEEE Computer Society, 2008: 1322-1328.
[8] Han H, Wang W Y, Mao B H. Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning[C]//LNCS 3644: Proceedings of the 2005 International Conference on Intelligent Computing, Hefei, Aug 23-26, 2005. Berlin, Heidelberg: Springer, 2005: 878-887.
[9] Yan Y T, Zhu Y W, Wu Z B, et al. Constructive covering algorithm-based SMOTE over-sampling method[J]. Journal of Frontiers of Computer Science and Technology,2020, 14(6): 975-984. 严远亭, 朱原玮, 吴增宝, 等. 构造性覆盖算法的SMOTE过采样方法[J]. 计算机科学与探索, 2020, 14(6): 975-984.
[10] Huang H S, Wei J A, Kang P D. New over-sampling SVM classification algorithm based on unbalanced data sample characteristics[J]. Control and Decision, 2018, 33(9): 16-25.黄海松, 魏建安, 康佩栋. 基于不平衡数据样本特性的新型过采样SVM分类算法[J]. 控制与决策, 2018, 33(9): 16-25.
[11] Barua S, Islam M M, Yao X, et al. MWMOTE majority weighted minority oversampling technique for imbalanced data set learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(2): 405-425.
[12] Zhu T, Lin Y, Liu Y. Synthetic minority oversampling technique for multiclass imbalance problems[J]. Pattern Recognition, 2017, 72: 327-340.
[13] Nekooeimehr I, Lai-yuen S K. Adaptive semiunsupervised weighted oversampling (A-SUWO) for imbalanced datasets[J]. Expert Systems with Applications, 2016, 46: 405-416.
[14] Wang C X , Zhang T, Ma C S. Improved SMOTE algorithm for imbalanced datasets[J]. Journal of Frontiers of Computer Science and Technology, 2014, 8(6): 91-98. 王超学, 张涛, 马春森. 面向不平衡数据集的改进型SMOTE算法[J]. 计算机科学与探索, 2014, 8(6): 91-98.
[15] Alcalá-Fdez J, Fernández A, Luengo J, et al. Keel data-mining software tool: data set repository, integration of algorithms and experimental analysis framework[J]. Journal of Multiple- Valued Logic & Soft Computing, 2011, 17: 255-287.
[16] Fernández A, García S, del Jesus M J, et al. A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced data-sets[J]. Fuzzy Sets and Systems, 2008, 159(18): 2378-2398.
[17] Cohen W W. Fast effective rule induction[M]. San Francisco: Morgan Kaufmann, 1995.
[18] Hall M, Frank E, Holmes G, et al. The WEKA data mining software: an update[J]. ACM SIGKDD Explorations Newsletter, 2009, 11(1): 10-18.
[19] Wu Y F, Liang J Y, Wang J H. Classification algorithm based on hybrid sampling for unbalanced data[J]. Journal of Frontiers of Computer Science and Technology, 2019, 13(2): 342-349. 吴艺凡, 梁吉业, 王俊红. 基于混合采样的非平衡数据分类算法[J]. 计算机科学与探索, 2019, 13(2): 342-349.
[20] Zhao N, Zhang X F, Zhang L J. Overview of imbalanced data classification[J]. Computer Science, 2018, 45(6A): 22-27.赵楠,张小芳,张利军. 不平衡数据分类研究综述[J]. 计算机科学,2018, 45(6A): 22-27.
[21] Li Y X, Chai Y, Hu Y Q, et al. Review of imbalanced data classification methods[J]. Control and Decision, 2019, 34(4): 673-688. 李艳霞,柴毅,胡友强,等. 不平衡数据分类方法综述[J]. 控制与决策, 2019, 34(4): 673-688.
[22] Sander J, Ester M, Kriegel H P, et al. Density-based clustering in spatial databases: the algorithm GDBSCAN and its applications[J]. Data Mining and Knowledge Discovery, 1998, 2(2): 169-194.
[23] Sun Y, Kamel M S, Wang Y. Boosting for learning multiple classes with imbalanced class distribution[C]//Proceedings of the 6th International Conference on Data Mining, Hong Kong, China, Dec 18-22, 2006. Washington: IEEE Computer Society, 2006: 592-602.
[24] Hand D J, Till R J. A simple generalisation of the area under the ROC curve for multiple class classification problems[J]. Machine Learning, 2001, 45(2): 171-186.
[25] Guo H, Liu H, Wu C, et al. Logistic discrimination based on G-mean and F-measure for imbalanced problem[J]. Journal of Intelligent & Fuzzy Systems, 2016, 31(3): 1155-1166.
[26] Bradley A P. The use of the area under the ROC curve in the evaluation of machine learning algorithms[J]. Pattern Recognition, 1997, 30(7): 1145-1159.
[27] Hanley J A, Mcneil B J. The meaning and use of the area under a receiver operating characteristic (ROC) curve[J]. Radiology, 1982, 143(1): 29-36. |