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In this study, we systematically investigate the impact of class imbalance on classification performance of convolutional neural networks (CNNs) and compare frequently used methods to address the issue.
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Toward scalable learning with non-uniform class and cost distributions: A case study in credit card fraud detection
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Data mining for direct marketing: Problems and solutions
Charles X Ling and Chenghui Li · 1998
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Neural network classification and prior class probabilities
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Cost-sensitive learning with neural networks
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Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Nathalie Japkowicz, Stephen Jose Hanson, and Mark A Gluck · 2000
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Charles Elkan · 2001
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Novelty detection using auto-associative neural network
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The problem of bias in training data in regression problems in medical decision support
Brian Mac Namee, Padraig Cunningham, Stephen Byrne, and Owen I Corrigan · 2002
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The class imbalance problem: A systematic study
Nathalie Japkowicz and Shaju Stephen · 2002
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Smote: synthetic minority over-sampling technique
Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer · 2002
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Learning when data sets are imbalanced and when costs are unequal and unknown
Marcus A Maloof · 2003
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Chris Drummond, Robert C Holte, et al · 2003
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Ricardo Barandela, E Rangel, José Salvador Sánchez, and Francesc J Ferri · 2003
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Smoteboost: Improving prediction of the minority class in boosting
Nitesh V Chawla, Aleksandar Lazarevic, Lawrence O Hall, and Kevin W Bowyer · 2003
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Auc: a statistically consistent and more discriminating measure than accuracy
Charles X Ling, Jin Huang, and Harry Zhang · 2003
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An approach to imbalanced data sets based on changing rule strength
Jerzy W Grzymala-Busse, Linda K Goodwin, Witold J Grzymala-Busse, and Xinqun Zheng · 2004
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Classification and knowledge discovery in protein databases
Predrag Radivojac, Nitesh V Chawla, A Keith Dunker, and Zoran Obradovic · 2004
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Class imbalances versus small disjuncts
Taeho Jo and Nathalie Japkowicz · 2004
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Learning from imbalanced data sets with boosting and data generation: the databoost-im approach
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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A hybrid classifier combining smote with pso to estimate 5-year survivability of breast cancer patients
Kung-Jeng Wang, Bunjira Makond, Kun-Huang Chen, and Kung-Min Wang · 2014
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Caffe: Convolutional architecture for fast feature embedding
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell · 2014
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Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2014
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Hongyu Guo and Herna L Viktor · 2004
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Data mining for imbalanced datasets: An overview
Nitesh V Chawla · 2005
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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
Cited alongside, same era.
Training cost-sensitive neural networks with methods addressing the class imbalance problem
Zhi-Hua Zhou and Xu-Ying Liu · 2006
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The novelty detection approach for different degrees of class imbalance
Hyoung-joo Lee and Sungzoon Cho · 2006
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Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance
Maciej A Mazurowski, Piotr A Habas, Jacek M Zurada, Joseph Y Lo, Jay A Baker, and Georgia D Tourassi · 2008
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Learning from imbalanced data
Haibo He and Edwardo A Garcia · 2009
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Jiuxiang Gu, Zhenhua Wang, Jason Kuen, Lianyang Ma, Amir Shahroudy, Bing Shuai, Ting Liu, Xingxing Wang, and Gang Wang · 2015
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Cost sensitive learning of deep feature representations from imbalanced data
Salman H Khan, Mohammed Bennamoun, Ferdous Sohel, and Roberto Togneri · 2015
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Cost-aware pre-training for multiclass cost-sensitive deep learning
Yu-An Chung, Hsuan-Tien Lin, and Shao-Wen Yang · 2015
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Age and gender classification using convolutional neural networks
Gil Levi and Tal Hassner · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Learning from class-imbalanced data: Review of methods and applications
Guo Haixiang, Li Yijing, Jennifer Shang, Gu Mingyun, Huang Yuanyue, and Gong Bing · 2016
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Towards effective classification of imbalanced data with convolutional neural networks
Vidwath Raj, Sven Magg, and Stefan Wermter · 2016
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Training deep neural networks on imbalanced data sets
Shoujin Wang, Wei Liu, Jia Wu, Longbing Cao, Qinxue Meng, and Paul J Kennedy · 2016
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Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases
Andrew Janowczyk and Anant Madabhushi · 2016
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Detection of concealed cars in complex cargo x-ray imagery using deep learning
Nicolas Jaccard, Thomas W Rogers, Edward J Morton, and Lewis D Griffin · 2016
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Relay backpropagation for effective learning of deep convolutional neural networks
Li Shen, Zhouchen Lin, and Qingming Huang · 2016
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Imagenet pre-trained models with batch normalization
Marcel Simon, Erik Rodner, and Joachim Denzler · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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The inaturalist challenge 2017 dataset
Grant Van Horn, Oisin Mac Aodha, Yang Song, Alex Shepard, Hartwig Adam, Pietro Perona, and Serge Belongie · 2017
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Brain tumor segmentation with deep neural networks
Mohammad Havaei, Axel Davy, David Warde-Farley, Antoine Biard, Aaron Courville, Yoshua Bengio, Chris Pal, Pierre-Marc Jodoin, and Hugo Larochelle · 2017
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Tree induction for probability-based ranking
Foster Provost and Pedro Domingos · 2017
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