[1] |
AKBANI R, KWEK S, JAPKOWICZ N. Applying support vector machine to imbalanced datasets[C]// Machine Learning: ECML 2004. Berlin: Springer, 2004: 39-50.
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[2] |
MAZUROWSKI M A, HABAS P A, ZURADA J M, et al. Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance[J]. Neural Networks, 2008, 21:427-436.
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[3] |
TAVALLAEE M, STAKHANVA N, GHORBANI A A. Toward credible evaluation of anomaly-based intrusion-detection methods[J]. IEEE Transactions on Systems, Man, and Cybernetics Part C, 2010, 40(5):516-524.
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[4] |
BERMEJO P,GSMEZ J A, PUERTA J M. Improving the performance of Naive Bayes multinomial in e-mail foldering by introducing distribution-based balance of datasets[J]. Expert Systems with Applications, 2011, 38(3): 2072-2080.
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[5] |
WEI W, LI J, CAO L, et al. Effective detection of sophisticated online banking fraud on extremely imbalanced data[J]. World Wide Web, 2013, 16: 449-475.
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[6] |
KERDPRASOP K, KERDPRASOP N. A data mining approach to automate fault detection model development in the semiconductor manufacturing process[J]. International Journal of Mechanics, 2011, 5(4): 336-344.
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[7] |
CHAWLA N V, JAPKOWICZ N, KOTCZ A. Editorial: Special issue on learning from imbalanced data sets[J]. ACM SIGKDD Explorations Newsletter, 2004, 6(1): 1-6.
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[8] |
DOUZAS G, BACAO F. Effective data generation for imbalanced learning using conditional generative adversarial networks[J]. Expert Systems with Applications, 2018, 91: 464-471.
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[9] |
CHAWLA N V. Data mining for imbalanced datasets: An overview[C]// Data Mining and Knowledge Discovery Handbook. Berlin: Springer, 2009: 875-886.
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[10] |
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.
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[11] |
HAN H, WANG W Y, MAO B H. Borderline-SMOTE: A new over-sampling method in imbalanced data sets learning[J]. ICIC 2005: Advances in Intelligent Computing, 2005, 17(12): 878-887.
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[12] |
HE H, BAI Y, GARCIA E A, et al. ADASYN: Adaptive synthetic sampling approach for imbalanced learning[C]// 2008 IEEE International Joint Conference on Neural Networks. IEEE, 2008: 1322-1328.
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[13] |
BATISTA G E, 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.
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[14] |
FAN W, ZHANG J, STOTFO S J, et al. AdaCost: Misclassification cost-sensitive boosting[C]// Proceedings of the 16th International Conference on Learning, Slovenia: Morgan Kaufmann, 1999: 97-105.
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[15] |
WOZNIAK M. Classifiers: Methods of data,knowledge, and classifier combination[M]. Berlin: Springer, 2013.
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[16] |
MARIANI G, SCHEIDEGGER F, ISTRATE R, et al. BAGAN: Data augmentation with balancing GAN[DB/OL]. [2020-05-01]. https://arxiv.org/abs/1803.09655.
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[17] |
RADFORD A, METZ L, CHINTALA S. Unsupervised representation learning with deep convolutional generative adversarial networks[DB/OL]. [2020-05-01]. https://arxiv.org/abs/1511.06434v1.
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[18] |
GAO Y , JIAO Y , WANG Y , et al. Deep generative learning via variational gradient flow[DB/OL]. [2020-05-01]. https://arxiv.org/abs/1901.08469.
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[19] |
ZHANG Y. Deep generative model for multi-class imbalanced learning[DB]// Open Access Master’s Theses, 2018: Paper 1277.
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[20] |
LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]// 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, 2017: 2980-2988.
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[21] |
FAWCETT T. An introduction to ROC analysis[J]. Pattern Recognition Letters, 2006, 27(8): 861-874.
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[22] |
赵海霞,石洪波,武建,等.基于条件生成对抗网络的不平衡学习研究[J/OL].控制与决策, 2019: https://doi.org/10.13195/j.kzyjc.2019.0522.
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[23] |
莫赞,盖彦蓉,樊冠龙.基于GAN-AdaBoost-DT不平衡分类算法的信用卡欺诈分类[J].计算机应用, 2019, 39(2):618-622.
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[24] |
李诒靖,郭海湘,李亚楠,等.一种基于Boosting的集成学习算法在不均衡数据中的分类[J].系统工程理论与实践, 2016, 36(1):189-199.
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[25] |
GOODFELLOW I, POUGET A J, MIRZA M, et al. Generative adversarial nets[J]. Advances in Neural Information Processing Systems Conference, 2014, 27:2672-2680.
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[26] |
SALANT S W, SWITZER S, REYNOLDS R J. Losses from horizontal merger:The effects of an exogenous change in industry structure on Cournot-Nash equilibrium[J]. The Quarterly Journal of Economics, 1983, 98(2):185-199.
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[27] |
MIRZA M, OSINDERO S. Conditional generative adversarial nets[C]// Proceedings of the Neural Information Processing Systems Deep Learning Workshop, 2014.
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[28] |
SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et al. Dropout a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research, 2014, 15(1): 1929-1958.
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[29] |
BERGSTRA J, YAMINS D, DAVID D C. Hyperopt: A Python library for optimizing the hyperparameters of machine learning algorithms[C]// Proceedings of the 12th Python in Science Conference, Austin, TX, 2012.
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[30] |
李航.统计学习方法[M].北京:清华大学出版社, 2012.
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[31] |
KINGMA D P, BA J. Adam: A method for stochastic optimization[C]// The 3rd International Conference on Learning Representations, San Diego, CA, 2015.
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[32] |
BIAU G, CADRE B, SANGNIER M, et al. Some theoretical properties of GANs[DB/OL]. [2020-05-01]. https://arxiv.org/abs/1803.07819.
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[33] |
TSYBAKOV A B. Introduction to Nonparametric Estimation[M].Berlin: Springer, 2008.
|
[34] |
VAN DER VAART A W, WELLNER J. Weak Convergence and Empirical Processes[M]. Berlin: Springer, 2000.
|
[35] |
GIN W E, NICKL R. Mathematical Foundations of Infinite Dimensional Statistical Models[M]. Cambridge: Cambridge University Press, 2015.
|
[36] |
FRIEDMAN J H. Greedy function approximation: A gradient boosting machine[J]. Annals of Statistics, 2001, 29(5): 1189-1232.)
|
[1] |
AKBANI R, KWEK S, JAPKOWICZ N. Applying support vector machine to imbalanced datasets[C]// Machine Learning: ECML 2004. Berlin: Springer, 2004: 39-50.
|
[2] |
MAZUROWSKI M A, HABAS P A, ZURADA J M, et al. Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance[J]. Neural Networks, 2008, 21:427-436.
|
[3] |
TAVALLAEE M, STAKHANVA N, GHORBANI A A. Toward credible evaluation of anomaly-based intrusion-detection methods[J]. IEEE Transactions on Systems, Man, and Cybernetics Part C, 2010, 40(5):516-524.
|
[4] |
BERMEJO P,GSMEZ J A, PUERTA J M. Improving the performance of Naive Bayes multinomial in e-mail foldering by introducing distribution-based balance of datasets[J]. Expert Systems with Applications, 2011, 38(3): 2072-2080.
|
[5] |
WEI W, LI J, CAO L, et al. Effective detection of sophisticated online banking fraud on extremely imbalanced data[J]. World Wide Web, 2013, 16: 449-475.
|
[6] |
KERDPRASOP K, KERDPRASOP N. A data mining approach to automate fault detection model development in the semiconductor manufacturing process[J]. International Journal of Mechanics, 2011, 5(4): 336-344.
|
[7] |
CHAWLA N V, JAPKOWICZ N, KOTCZ A. Editorial: Special issue on learning from imbalanced data sets[J]. ACM SIGKDD Explorations Newsletter, 2004, 6(1): 1-6.
|
[8] |
DOUZAS G, BACAO F. Effective data generation for imbalanced learning using conditional generative adversarial networks[J]. Expert Systems with Applications, 2018, 91: 464-471.
|
[9] |
CHAWLA N V. Data mining for imbalanced datasets: An overview[C]// Data Mining and Knowledge Discovery Handbook. Berlin: Springer, 2009: 875-886.
|
[10] |
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.
|
[11] |
HAN H, WANG W Y, MAO B H. Borderline-SMOTE: A new over-sampling method in imbalanced data sets learning[J]. ICIC 2005: Advances in Intelligent Computing, 2005, 17(12): 878-887.
|
[12] |
HE H, BAI Y, GARCIA E A, et al. ADASYN: Adaptive synthetic sampling approach for imbalanced learning[C]// 2008 IEEE International Joint Conference on Neural Networks. IEEE, 2008: 1322-1328.
|
[13] |
BATISTA G E, 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.
|
[14] |
FAN W, ZHANG J, STOTFO S J, et al. AdaCost: Misclassification cost-sensitive boosting[C]// Proceedings of the 16th International Conference on Learning, Slovenia: Morgan Kaufmann, 1999: 97-105.
|
[15] |
WOZNIAK M. Classifiers: Methods of data,knowledge, and classifier combination[M]. Berlin: Springer, 2013.
|
[16] |
MARIANI G, SCHEIDEGGER F, ISTRATE R, et al. BAGAN: Data augmentation with balancing GAN[DB/OL]. [2020-05-01]. https://arxiv.org/abs/1803.09655.
|
[17] |
RADFORD A, METZ L, CHINTALA S. Unsupervised representation learning with deep convolutional generative adversarial networks[DB/OL]. [2020-05-01]. https://arxiv.org/abs/1511.06434v1.
|
[18] |
GAO Y , JIAO Y , WANG Y , et al. Deep generative learning via variational gradient flow[DB/OL]. [2020-05-01]. https://arxiv.org/abs/1901.08469.
|
[19] |
ZHANG Y. Deep generative model for multi-class imbalanced learning[DB]// Open Access Master’s Theses, 2018: Paper 1277.
|
[20] |
LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]// 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, 2017: 2980-2988.
|
[21] |
FAWCETT T. An introduction to ROC analysis[J]. Pattern Recognition Letters, 2006, 27(8): 861-874.
|
[22] |
赵海霞,石洪波,武建,等.基于条件生成对抗网络的不平衡学习研究[J/OL].控制与决策, 2019: https://doi.org/10.13195/j.kzyjc.2019.0522.
|
[23] |
莫赞,盖彦蓉,樊冠龙.基于GAN-AdaBoost-DT不平衡分类算法的信用卡欺诈分类[J].计算机应用, 2019, 39(2):618-622.
|
[24] |
李诒靖,郭海湘,李亚楠,等.一种基于Boosting的集成学习算法在不均衡数据中的分类[J].系统工程理论与实践, 2016, 36(1):189-199.
|
[25] |
GOODFELLOW I, POUGET A J, MIRZA M, et al. Generative adversarial nets[J]. Advances in Neural Information Processing Systems Conference, 2014, 27:2672-2680.
|
[26] |
SALANT S W, SWITZER S, REYNOLDS R J. Losses from horizontal merger:The effects of an exogenous change in industry structure on Cournot-Nash equilibrium[J]. The Quarterly Journal of Economics, 1983, 98(2):185-199.
|
[27] |
MIRZA M, OSINDERO S. Conditional generative adversarial nets[C]// Proceedings of the Neural Information Processing Systems Deep Learning Workshop, 2014.
|
[28] |
SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et al. Dropout a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research, 2014, 15(1): 1929-1958.
|
[29] |
BERGSTRA J, YAMINS D, DAVID D C. Hyperopt: A Python library for optimizing the hyperparameters of machine learning algorithms[C]// Proceedings of the 12th Python in Science Conference, Austin, TX, 2012.
|
[30] |
李航.统计学习方法[M].北京:清华大学出版社, 2012.
|
[31] |
KINGMA D P, BA J. Adam: A method for stochastic optimization[C]// The 3rd International Conference on Learning Representations, San Diego, CA, 2015.
|
[32] |
BIAU G, CADRE B, SANGNIER M, et al. Some theoretical properties of GANs[DB/OL]. [2020-05-01]. https://arxiv.org/abs/1803.07819.
|
[33] |
TSYBAKOV A B. Introduction to Nonparametric Estimation[M].Berlin: Springer, 2008.
|
[34] |
VAN DER VAART A W, WELLNER J. Weak Convergence and Empirical Processes[M]. Berlin: Springer, 2000.
|
[35] |
GIN W E, NICKL R. Mathematical Foundations of Infinite Dimensional Statistical Models[M]. Cambridge: Cambridge University Press, 2015.
|
[36] |
FRIEDMAN J H. Greedy function approximation: A gradient boosting machine[J]. Annals of Statistics, 2001, 29(5): 1189-1232.)
|