Japkowicz N, Stephen S. The class imbalance problem: a systematic study. Intelligent Data Analysis. 2002;6(5):429–49.

Article
Google Scholar

Japkowicz N. The Class Imbalance Problem: Significance and Strategies. In: Proc. of the Int'l Conf. on Artificial Intelligence, 2000.

Liu X-Y, Zhou Z-H, Wu J. Exploratory Undersampling for Class-Imbalance Learning. IEEE Transactions on Systems, Man, and Cybernetics. Part B (Cybernetics). 2009;39(2):539–50.

Article
Google Scholar

Guo X, Yin Y, Dong C, Yang G, Guangtong Z. On the Class Imbalance Problem. In: 2008 Fourth International Conference on Natural Computation, 2008.

Anand R, Mehrotra KG, Mohan CK, Ranka S. An Improved Algorithm for Neural Network Classification of Imbalanced Training Sets Rangachari h a n. In: IEEE TRANSACTIONS ON NEURAL NETWORKS, vol. 4, no. 6, 1993.

Johnson JM, Khoshgoftaar TM. Survey on deep learning with class imbalance. J Big Data. 2019;6:27.

Article
Google Scholar

Buda M, Maki A, Mazurowski MA. A systematic study of the class imbalance problem in convolutional neural networks. Neural Netw. 2018;106:249–59.

Article
Google Scholar

Ren M, Zeng W, Yang B, Urtasun R. Learning to Reweight Examples for Robust Deep Learning. In: Proceedings of the 35th International Conference on Machine Learning, p. 4334–4343, 2018.

Goodfellow IJ, Pouget-Abadie J, Mizra M, Xu B, Warde-Farley D, Ozair S, Courville and Y. Bengio, "Generative Adversarial Networks. In: Proceedings of the International Conference on Neural Information Processing Systems (NIPS 2014), p. 2672–2680, 2014.

Scott M, Plested J. GAN-SMOTE: A Generative Adversarial Network approach to Synthetic Minority Oversampling for One-Hot Encoded Data. In: ICONIP2019 Proceedings, 2019.

Haldar M, Abdool M, Ramanathan P, Xu T, Yang S, Duan H, Zhang Q, Barrow-Williams N, Turnbull BC, Collin BM, Legrand T. Applying Deep Learning To Airbnb Search," in KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019.

Arik S, Pfister T. TabNet: Attentive Interpretable Tabular Learning. In: Association for the Advancement of Artifical Intelligence; 2020.

Popov S, Morozov S, Babenko A. Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data. In: International Conference on Learning Representations; 2019.

He H, Garcia EA. Learning from imbalanced data. IEEE Trans Knowl Data Eng. 2009;21(9):1263–84.

Article
Google Scholar

Krawczyk B. Learning from imbalanced data: open challenges and future directions. Progr Artif Intell. 2016;5:221–32.

Article
Google Scholar

Kubat M, Matwin S. Addressing the Curse of Imbalanced Training Sets: One-Sided Selection. In: Proceedings of the Fourteenth International Conference on Machine Learning, pp. 179 - 186, 1997.

Chawla NV, Japkovicz N, Kotcz A. Editorial: special issue on learning from imbalanced data sets. In: ACM SIGKDD Explorations Newsletter; vol. 6, no. 1, 2004.

Van Hulse J, Khoshgoftaar TM, Napolitano A. Experimental perspectives on learning from imbalanced data. In: ICML '07: Proceedings of the 24th international conference on Machine learning, p. 935–942, 2007.

Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: Synthetic Minority Over-sampling Technique. J Artif Intell Res. 2002;16:331–57.

MATH
Google Scholar

Han H, Wang W-Y, Mao B-H. Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning. In: Lecture Notes in Computer Science; 2005. p. 878–87.

Jo T, Japkovicz N. Class imbalances versus small disjuncts. ACM SIGKDD Explorations Newsl. 2004;6(1):40–9.

Article
Google Scholar

Wilson DL. Asymptotic Properties of Nearest Neighbor Rules Using Edited Data. IEEE Trans Syst Man Cybern. 1972;2(3):408–21.

Article
MathSciNet
Google Scholar

Tomek I. Two Modifications of CNN. IEEE Trans Syst Man Cybern. 1971;6(11):769–72.

MathSciNet
MATH
Google Scholar

Tsai C-F, Lin W-C, Ke S-W. Big data mining with parallel computing: a comparison of distributed and MapReduce methodologies. J Syst Softw. 2016;122:83–92.

Article
Google Scholar

Yin L, Ge Y, Xiao K, Wang X, Quan X. Feature selection for high-dimensional imbalanced data. Neurocomputing. 2013;105:3–11.

Article
Google Scholar

Miller AI. Ian Goodfellow’s Generative Adversarial Networks: AI Learns to Imagine. Cambridge: MIT Press; 2019.

Google Scholar

Wang Z, She Q, Ward TE. Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy," ACM Computing Survey, 2020.

Sampath V, Maurtua I, Aguilar Martín JJ, Gutierrez A. A survey on generative adversarial networks for imbalance problems in computer vision tasks. In: J Big Data; 2021.

Odena A, Olah C, Shlens J. Conditional Image Synthesis with Auxiliary Classifier GANs. In: Proceedings of the 34 th International Conference on Machine Learning; 2017.

Antoniou A, Storkey A, Edwards H. Data Augmentation Generative Adversarial Networks. In: International Conference on Learning Representations; 2018.

Mariani G, Scheidegger F, Istrate R, Bekas C, Malossi C. BAGAN: Data Augmentation with Balancing GAN. ArXiv, abs/1803.09655; 2018.

Mullick SS, Datta S, Das S. Generative Adversarial Minority Oversampling. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV); 2019. p. 1695–704.

Ando S, Huang CY. Deep Over-sampling Framework for Classifying Imbalanced Data. In: Lecture Notes in Computer Science , vol. 40534; 2017.

Cieslak DA, Chawla NV, Striegel A. Combating Imbalance in Network Intrusion Datasets. In: 2006 IEEE International Conference on Granular Computing 2006. p. 732–7.

Khoshgoftaar TM, Leevy JL. A survey and analysis of intrusion detection models based on CSE-CIC-IDS2018 Big Data. J Big Data; 2020.

Vu L, Bui CT, Nguyen U. A Deep Learning Based Method for Handling Imbalanced Problem in Network Traffic Classification. In: SolCT; 2017.

Alshammari R, Zincir-Heywood AN. Can encrypted traffic be identified without port numbers, IP addresses and payload inspection? Comput Netw. 2010;55(6):1326–50.

Article
Google Scholar

More A. Survey of resampling techniques for improving classification performance in unbalanced datasets. In: Computing Research Repository, vol. abs/1608.06048, 2016.

Lee J, Park K. GAN-based imbalanced data intrusion detection system. Pers Ubiquit Comput. 2019;25:121–8.

Article
Google Scholar

Sharafaldin I, Lashkari AH, Ghorbani AA. Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization. In: 4th International Conference on Information Systems Security and Privacy (ICISSP); 2018.

Wang Z, Wang P, Zhou X, Li S, Zhang M. FLOWGAN:Unbalanced network encrypted traffic identification method based on GAN. In: 2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom); 2019.

Draper-Gil G, Lashkari AH, Mamun MSI, Ghorbani AA. Characterization of Encrypted and VPN Traffic using Time-relatedFeatures. In: International Conference on Information Systems Security and Privacy (ICISSP 2016). p. 407–14.

Wang P, Li S, Ye F, Wang Z, Zhang M. PacketCGAN: Exploratory Study of Class Imbalance for Encrypted Traffic Classification Using CGAN. In: ICC 2020 - 2020 IEEE International Conference on Communications (ICC); 2020. p. 1–7.

Wang W, Zhu M, Zeng X, Ye X, Sheng Y. Malware traffic classification using convolutional neural network for representation learning. In: International Conference on Information Networking; 2017.

Yilmaz I, Masum R, Siraj A. Addressing Imbalanced Data Problem with Generative Adversarial Network For Intrusion Detection. In: 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI); 2020.

Macia Ferndandez G, Camacho J, Magan-Carrion R, Garcia-Teodoro P, Theron R. Ugr'16: a new dataset for the evaluation of cyclostationarity-based network IDSs. In: Computers & Security; 2017.

Belenko V, Chernenko V, Kalinin M, Krundyshev V. Evaluation Of GAN Applicability for Intrusion Detection in Self-Organizing Networks of Cyber Physical Systems. In: 2018 International Russian Automation Conference (RusAutoCon); 2018.

Jegadeesan K, Ayothi S. An Empirical Study of Methods, Metrics and Evaluation of Data Mining Techniques in Credit Card Fraudulence Detection. J Adv Res Dynam Control Syst. 2020;12:7.

Article
Google Scholar

Fiore, U, De Santis A, Perla F, Zanetti P, Palmieri F. Using generative adversarial networks for improving classification effectiveness in credit card fraud detection. In: Information sciences; 2019. p. 448–55.

Dal Pozzolo A, Caelen O, Bontempi G. Calibrating Probability with Undersamplingfor Unbalanced Classification. In: IEEE Symposium Series on Computational Intelligence, 2015.

Lei K, Xie Y, Zhong S, Dai J, Yang M, Shen Y. Generative adversarial fusion network for class imbalance credit scoring. Neural Comput Appl. 2020;32:8451–62.

Article
Google Scholar

Odena A. Semi-Supervised Learning with Generative Adversarial Networks. In: Data Efficient Machine Learning workshop at ICML 2016, 2016.

Yeh I-C, Lien C-H. The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert Sys Appl. 2009;36(2):2473–80.

Article
Google Scholar

Engelmann J, Lessmann S. Conditional Wasserstein GAN-based oversampling of tabular data for Imbalanced Learning. In: Expert Systems With Applications, 2021.

Quintana M, Miller C. Towards Class-Balancing Human Comfort Datasets with GANs. In: The 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys ’19); 2019.

Arjovsky M, Chintala S, Bottou L. Wasserstein GAN. In: International conference on machine learning. PMLR; 2017. p. 214–23.

Jang E, Gu S, Poole B. Categorical Reparameterization with Gumbel-Softmax. In: International Conference on Learning Representations; 2017.

Mottini A, Lheritier A, Acuna-Agost R. Airline Passenger Name Record Generation using Generative Adversarial Networks. In: ICML 2018 - workshop on Theoretical Foundations and Applications of Deep Generative Models; 2018.

Lopez V, Fernandez A, Garcia S, Palade V, Herrera F. An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics. Inf Sci. 2013;250:113–41.

Article
Google Scholar

Wang C, Yu Z, Zheng H, Wang N, Zheng B. CGAN-PLANKTON: Towards Large-Scale Imbalanced Class Generation and Fine-Grained Classification. In: 2017 IEEE International Conference on Image Processing (ICIP); 2017. p. 855–9.

Orenstein ECC, Beijbom O, Peacock EE, Sosik HM. WHOI-Plankton- A Large Scale Fine Grained Visual Recognition Benchmark Dataset for Plankton Classification. In: Third Workshop on Fine-Grained Visual Categorization at CVPR 2015, 2015.

Munir S, Tran L, Francis J, Shelton C, Singh Arora R, Helsing C, Quintana M, Krishnan Prakash A, Rowe A, Berges M. Fine grained Occupancy estimatoR using Kinect on ARM Embedded Platforms. In: BuildSys 17 Proceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments]; 2017.

Xu L, Veeramachaneni K. Synthesizing Tabular Data using Generative Adversarial Networks. ArXiv, vol. abs/1811.11264; 2018.

Quintana M, Wai Tham K, Schiavon S, Miller C. Balancing thermal comfort datasets: We GAN, but should we? In: Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation; 2020.

dos Santos Tanaka FHK, Arahna C. Data Augmentation Using GANs. In: Proceedings of Machine Learning Research XXX; 2019. p. 1–16.

Smith JW, Everhart J, Dickson W, Knowler W, Johannes R. Using the ADAP Learning Algorithm to Forecast the Onset of Diabetes Mellitus. In: Proc Annu Symp Comput Appl Med Care, pp. 261–265, 1988.

Dheeru D, Graff C. UCI machine learning repository. Irvine: University of California, Irvine, School of Information and Computer Sciences, 2017.

Dal Pozzolo A, Boracchi G, Caelen O, Alippi C, Bontepi G. Credit card fraud detection: A realistic modeling and a novel learning strategy. IEEE Transactions on Neural Networks and Learning Systems; 2017. p. 1–14.

Deepshikha K, Naman A. Removing Class Imbalance using Polarity-GAN: An Uncertainty Sampling Approach. Conference on Computer Vision and Pattern Recognition; 2020.

Lopez Chau A, Li X, Yu W, Cervantes J, Mejia-Alvarez P. Border samples detection for data mining applications using non convex hulls. Mexican International Conference on Artificial Intelligence; 2011. p. 261–72.

Xu L, Skoularidou M, Cuesta-Infante A, Veeramachaneni K. Modeling Tabular Data using Conditional GAN. In: 33rd Conference on Neural Information Processing Systems (NeurIPS 2019); 2019.

Bishop MC. Pattern recognition and machine learning. New York: Springer Science+Business Media, LLC; 2006.

MATH
Google Scholar

Kingma DP, Welling M. Auto-encoding variational bayes. In: International Conference on Learning Representations; 2013.

Srivastava A, Valkov L, Russell C, Gutmann MU, Sutton C. Veegan: Reducing mode collapse in gans using implicit variational learning. In: Advances in Neural Information Processing Systems; 2017.

Jordon J, Yoon J, van der Schaar M. Pate-gan: Generating synthetic data with differential privacy guarantees. In: International Conference on Learning Representations; 2019.

Ren J, Liu Y, Liu J. EWGAN: Entropy-Based Wasserstein GAN for Imbalanced Learning. In: The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19); 2019.

Montahaei E, Ghorbani M, Baghshah MS, Rabiee HR. Adversarial Classifier for Imbalanced Problems. *arXiv,* vol. abs/1811.08812; 2018.

Schlegl T, Seebock P, Waldstein SM, Schmidt-Erfurth U, Langs G. Unsupervised Anomaly Detection withGenerative Adversarial Networks to GuideMarker Discovery. In: Information Processing in Medical Imaging; 2021.

Mizra B, Haroon D, Khan B, Padhani A, Syed TQ. Deep generative models to counter class imbalance: a model-metric mapping with proportionality calibration methodology. In: IEEE Access; 2015. p. 55879–97.

Zhu J-Y, Park T, Isola P, Efros AA. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. In: IEEE International Conference on Computer Vision (ICCV), 2017; 2017.

Redford A, Metz L, Chintala S. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. In: International Conference on Learning Representations 2016; 2015.

He H, Bai Y, Garcia EA, Li S.ADASYN: Adaptive Synthetic Sampling Approach for Imbalanced Learning. In: IEEE World Congress on Computational Intelligence; 2008.

Osindero S, Mirza M. Conditional Generative Adversarial Nets. arXiv:1411.1784 [cs, stat]; 2014.

Douzas G, Bacao F. Effective data generation for imbalanced learning using conditional generative adversarial networks. Expert Syst Appl. 2017;91:464–71.

Article
Google Scholar

Salimans T, Goodfellow I, Zaremba W, Radford A, Chen X. Improved Techniques for training GANs. In: Advances in Neural Information Processing Systems (NIPS); 2016.

Vacarri I, Orani V, Paglialonga A, Cambiaso E, Mongelli M. A Generative Adversarial Network (GAN) Technique for nternet of Medical Things Data. Sensors. 2021;21:3726.

Article
Google Scholar

Park N, Mohammadi M, Gorde K, Jajodia S, Park J, Kim Y. Data Synthesis based on Generative Adversarial Networks.. In: 44th International Conference on Very Large Data Bases 2018; 2018.

Okerinde A, Shamir L, Hsu W, Theis T, Nafi N. eGAN: Unsupervised approach to class imbalance using transfer learning. In: 2021 The 19th International Conference on Computer Analysis of Images and Patterns (CAIP); 2021.

Khoshgoftaar TM, Seiffert C, Van Hulse J, Napolitano A, Folleco A.Learning with limited minority class data. In: Sixth International Conference on Machine Learning and Applications (ICMLA 2007); 2007.

Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. Adv Neural Inf Proces Syst. 2017;30:8.

Google Scholar

Sajjadi MS, Bachem O, Lucic M, Bousquet O, Gelly S.Assessing Generative Models via Precision and Recall. In: 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montreal; 2018.

Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E. Scikit-learn: Machine Learning in Python. J Mach Learn Res. 2011;12:2825–30.

MathSciNet
MATH
Google Scholar