Synthetic minority oversampling of vital statistics data with generative adversarial networks
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Tapio Pahikkala | Antti Airola | Aki Koivu | Mikko Sairanen | T. Pahikkala | A. Airola | M. Sairanen | Aki Koivu
[1] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[2] K. Nicolaides,et al. Prediction of stillbirth from maternal demographic and pregnancy characteristics , 2016, Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology.
[3] Stefan Schaal,et al. Encyclopedia of Machine Learning , 2010 .
[4] Jian Sun,et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[5] M. Mukaka,et al. Statistics corner: A guide to appropriate use of correlation coefficient in medical research. , 2012, Malawi medical journal : the journal of Medical Association of Malawi.
[6] Jasper Snoek,et al. Practical Bayesian Optimization of Machine Learning Algorithms , 2012, NIPS.
[7] Gary M. Weiss,et al. Cost-Sensitive Learning vs. Sampling: Which is Best for Handling Unbalanced Classes with Unequal Error Costs? , 2007, DMIN.
[8] Richard J. Anderson. At the University of Arkansas , 1980 .
[9] K. Nicolaides,et al. Prediction of stillbirth from biochemical and biophysical markers at 11–13 weeks , 2016, Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology.
[10] Christopher M. Bishop,et al. Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .
[11] C. Weinberg,et al. Risk factors for antepartum and intrapartum stillbirth. , 1993, American journal of epidemiology.
[12] R. Fletcher. Practical Methods of Optimization , 1988 .
[13] G. Colditz,et al. A stillbirth calculator: Development and internal validation of a clinical prediction model to quantify stillbirth risk , 2017, PloS one.
[14] Rok Blagus,et al. Evaluation of SMOTE for High-Dimensional Class-Imbalanced Microarray Data , 2012, 2012 11th International Conference on Machine Learning and Applications.
[15] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[16] Victor S. Sheng,et al. Cost-Sensitive Learning and the Class Imbalance Problem , 2008 .
[17] Daniela Fischer,et al. Digital Design And Computer Architecture , 2016 .
[18] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[19] Zhi-Hua Zhou,et al. Ieee Transactions on Knowledge and Data Engineering 1 Training Cost-sensitive Neural Networks with Methods Addressing the Class Imbalance Problem , 2022 .
[20] Joseph E. Hoag,et al. Synthetic data generation: theory, techniques and applications , 2008 .
[21] Taghi M. Khoshgoftaar,et al. Experimental perspectives on learning from imbalanced data , 2007, ICML '07.
[22] Léon Bottou,et al. Towards Principled Methods for Training Generative Adversarial Networks , 2017, ICLR.
[23] Asad Malik,et al. Maternal and fetal risk factors for stillbirth: population based study , 2013, BMJ.
[24] Andrew L. Maas. Rectifier Nonlinearities Improve Neural Network Acoustic Models , 2013 .
[25] Richard Hans Robert Hahnloser,et al. Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit , 2000, Nature.
[26] Lei Xu,et al. Synthesizing Tabular Data using Generative Adversarial Networks , 2018, ArXiv.
[27] Nathalie Japkowicz,et al. The class imbalance problem: A systematic study , 2002, Intell. Data Anal..
[28] Sepp Hochreiter,et al. Self-Normalizing Neural Networks , 2017, NIPS.
[29] Aaron C. Courville,et al. Improved Training of Wasserstein GANs , 2017, NIPS.
[30] C. Kambhampati,et al. Balancing Class for Performance of Classification with a Clinical Dataset , 2022 .
[31] D. Grobbee,et al. Predicting stillbirth in a low resource setting , 2016, BMC Pregnancy and Childbirth.
[32] J. DiSantostefano,et al. International Classification of Diseases 10th Revision (ICD-10) , 2009 .
[33] K. Nicolaides,et al. Prediction of miscarriage and stillbirth at 11–13 weeks and the contribution of chorionic villus sampling , 2011, Prenatal diagnosis.
[34] S. Finklestein,et al. The predictive power of diagnostic tests and the effect of prevalence of illness. , 1983, Archives of general psychiatry.
[35] M. Ezzati,et al. Major risk factors for stillbirth in high-income countries: a systematic review and meta-analysis , 2011, The Lancet.
[36] Paul Babyn,et al. Generative Adversarial Network in Medical Imaging: A Review , 2018, Medical Image Anal..
[37] Charles X. Ling,et al. Data Mining for Direct Marketing: Problems and Solutions , 1998, KDD.
[38] Aki Koivu,et al. Predicting risk of stillbirth and preterm pregnancies with machine learning , 2020, Health Information Science and Systems.
[39] Gary L Darmstadt,et al. Reducing stillbirths: screening and monitoring during pregnancy and labour , 2009, BMC pregnancy and childbirth.
[40] S. Saleem,et al. Stillbirth in developing countries: a review of causes, risk factors and prevention strategies , 2009, The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians.