Table 11 ID labels used in digraph above, with corresponding paper names, Part II
From: The use of generative adversarial networks to alleviate class imbalance in tabular data: a survey
Paper ID | Paper Name |
|---|---|
48 | Began: boundary equilibrium generative adversarial networks |
49 | A survey of predictive modeling on imbalanced domains |
50 | Costaware pretraining for multiclass costsensitive deep learning |
51 | Imbalanced deep learning by minority class incremental rectification |
52 | SMOTE for learning from imbalanced data: Progress and challenges marking the 15year anniversary |
53 | Gans trained by a two timescale update rule converge to a local nash equilibrium |
54 | Learning deep representation for imbalanced classification |
55 | Learning from imbalanced data: open challenges and future directions |
56 | Least squares generative adversarial networks |
57 | Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance |
58 | Reading digits in natural images with unsupervised feature learning |
59 | A classificationbased study of covariate shift in gan distributions |
60 | Learning to model the tail |
61 | Fashionmnist: a novel image dataset for benchmarking machine learning algorithms |
62 | Holisticallynested edge detection |
63 | A Deep Learning Based Method for Handling Imbalanced Problem in Network Traffic Classification |
64 | Deep learning |
65 | Learning from imbalanced data for encrypted traffic identification problem |
66 | Service Name and Transport Protocol Port Number Registry |
67 | Towards automated application signature generation for traffic identification |
68 | FLOWGAN:Unbalanced Network Encrypted Traffic Identification Method Based on GAN |
69 | Mobile encrypted traffic classification using deep learning |
70 | The class imbalance problem: a systematic study |
71 | Datanet: Deep learning based encrypted network traffic classification in sdn home gateway |
72 | Endtoend encrypted traffic classification with onedimensional convolution neural networks |
73 | Network traffic classifier with convolutional and recurrent neural networks for internet of things |
74 | A hierarchical approach to encrypted data packet classification in smart home gateways |
75 | Characterization of encrypted and vpn traffic using timerelated features |
76 | PacketCGAN: Exploratory Study of Class Imbalance for Encrypted Traffic Classification Using CGAN |
77 | Using generative adversarial networks for improving classification effectiveness in credit card fraud detection |
78 | Calibrating probability with undersampling for unbalanced classification |
79 | An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics |
80 | Generative adversarial fusion network for class imbalance credit scoring |
81 | Balancing training data for automated annotation of keywords: a case study |
82 | Benchmarking stateoftheart classification algorithms for credit scoring: an update of research |
83 | Conditional Wasserstein GANbased oversampling of tabular data for imbalanced learning |
84 | A StyleBased Generator Architecture for Generative Adversarial Networks |
85 | Modeling Tabular data using Conditional GAN |
86 | Supervised Class Distribution Learning for GANsBased Imbalanced Classification |
87 | Learning from classimbalanced data: Review of methods and applications |
88 | Deep Generative Models to Counter Class Imbalance: A ModelMetric Mapping With Proportion Calibration Methodology |
89 | Improving imbalanced learning through a heuristic oversampling method based on kmeans and SMOTE |
90 | Borderline oversampling for imbalanced data classification |
91 | An instance level analysis of data complexity |
92 | Adversarial Classifier for Imbalanced Problems |
93 | Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery |