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Class imbalance is a common problem in supervised learning and impedes the predictive performance of classification models.
Modeling Tabular data using Conditional GAN
Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni · 1907
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Self-Ensembling with GAN-based Data Augmentation for Domain Adaptation in Semantic Segmentation
Jaehoon Choi, Taekyung Kim, and Changick Kim · 1909
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Random Forests
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N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer · 2002
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The class imbalance problem: A systematic study
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Credit scoring and its applications
Lyn C Thomas, David B Edelman, and Jonathan N Crook · 2002
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Benchmarking state-of-the-art classification algorithms for credit scoring
Bart Baesens, Tony Van Gestel, Stijn Viaene, M. STEPANOVA, Johan Suykens, and Jan Vanthienen · 2003
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Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning
Hui Han, Wen-Yuan Wang, and Bing-Huan Mao · 2005
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The relationship between Precision-Recall and ROC curves
Jesse Davis and Mark Goadrich · 2006
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Statistical Comparisons of Classifiers over Multiple Data Sets
Janez Demšar · 2006
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ADASYN: Adaptive synthetic sampling approach for imbalanced learning
Haibo He, Yang Bai, Edwardo A. Garcia, and Shutao Li · 2008
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Learning from Imbalanced Data
Haibo He and Edwardo A. Garcia · 2009
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition
Trevor Hastie, Robert Tibshirani, and Jerome Friedman · 2009
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The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients
I.-Cheng Yeh and Che-hui Lien · 2009
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Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power
Salvador García, Alberto Fernández, Julián Luengo, and Francisco Herrera · 2010
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Classification of imbalanced data: a review
Yanminsun, Andrew Wong, and Mohamed S. Kamel · 2011
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Scikit-learn: Machine Learning in Python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Édouard Duchesnay · 2011
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An experimental comparison of classification algorithms for imbalanced credit scoring data sets
Iain Brown and Christophe Mues · 2012
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On the suitability of resampling techniques for the class imbalance problem in credit scoring
A I Marqués, V. García, and J S Sánchez · 2013
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Using semi-supervised classifiers for credit scoring
K. Kennedy, B. Mac Namee, and S. J. Delany · 2013
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An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics
Victoria López, Alberto Fernández, Salvador García, Vasile Palade, and Francisco Herrera · 2013
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Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
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Generative Adversarial Nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Conditional Generative Adversarial Nets
Mehdi Mirza and Simon Osindero · 2014
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Categorical Reparameterization with Gumbel-Softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2017
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The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
Chris J. Maddison, Andriy Mnih, and Yee Whye Teh · 2017
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Language Generation with Recurrent Generative Adversarial Networks without Pre-training
Ofir Press, Amir Bar, Ben Bogin, Jonathan Berant, and Lior Wolf · 2017
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba · 2017
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UCI Machine Learning Repository
Dheeru Dua and Casey Graff · 2017
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Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research
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Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Learning from class-imbalanced data: Review of methods and applications
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Effective data generation for imbalanced learning using Conditional Generative Adversarial Networks
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Generative adversarial network based telecom fraud detection at the receiving bank
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Synthesizing Tabular Data using Generative Adversarial Networks
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Data synthesis based on generative adversarial networks
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Generating Multi-label Discrete Patient Records using Generative Adversarial Networks
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Airline Passenger Name Record Generation using Generative Adversarial Networks
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Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step
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A Style-Based Generator Architecture for Generative Adversarial Networks
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
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