Synthetic sampling from small datasets: A modified mega-trend diffusion approach using k-nearest neighbors
暂无分享,去创建一个
Jayanth Sivakumar | Karthik Ramamurthy | Menaka Radhakrishnan | Daehan Won | Daehan Won | M. Radhakrishnan | Jayanth Sivakumar | K. Ramamurthy
[1] Der-Chiang Li,et al. Rebuilding sample distributions for small dataset learning , 2018, Decis. Support Syst..
[2] Der-Chiang Li,et al. Employing box-and-whisker plots for learning more knowledge in TFT-LCD pilot runs , 2012 .
[3] Der-Chiang Li,et al. Employing virtual samples to build early high-dimensional manufacturing models , 2013 .
[4] Brian Johnson,et al. Classifying a high resolution image of an urban area using super-object information , 2013 .
[5] Adi Wijaya,et al. Behavior Determinant Based Cervical Cancer Early Detection with Machine Learning Algorithm , 2016 .
[6] Luís Torgo,et al. SMOTE for Regression , 2013, EPIA.
[7] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[8] Der-Chiang Li,et al. The Generalized-Trend-Diffusion modeling algorithm for small data sets in the early stages of manufacturing systems , 2010, Eur. J. Oper. Res..
[9] Jörg Drechsler,et al. An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets , 2011, Comput. Stat. Data Anal..
[10] Giuseppe Jurman,et al. Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone , 2020, BMC Medical Informatics and Decision Making.
[11] Brian Johnson,et al. High-resolution urban land-cover classification using a competitive multi-scale object-based approach , 2013 .
[12] Firuz Kamalov,et al. Gamma distribution-based sampling for imbalanced data , 2020, Knowl. Based Syst..
[13] M. Elter,et al. The prediction of breast cancer biopsy outcomes using two CAD approaches that both emphasize an intelligible decision process. , 2007, Medical physics.
[14] Ahyeon Koh,et al. Sweat and saliva cortisol response to stress and nutrition factors , 2020, Scientific Reports.
[15] Alok Baveja,et al. Computing , Artificial Intelligence and Information Technology A data-driven software tool for enabling cooperative information sharing among police departments , 2002 .
[16] Roberto Todeschini,et al. Investigating the mechanisms of bioconcentration through QSAR classification trees. , 2016, Environment international.
[17] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[18] Francisco Charte,et al. MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation , 2015, Knowl. Based Syst..
[19] Yu Cheng,et al. Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records , 2017, 2017 IEEE International Conference on Data Mining (ICDM).
[20] Linda Coyle,et al. Generation and evaluation of synthetic patient data , 2020, BMC Medical Research Methodology.
[21] David Gil Méndez,et al. Predicting seminal quality with artificial intelligence methods , 2012, Expert Syst. Appl..
[22] Saeid Nahavandi,et al. An expert system for selecting wart treatment method , 2017, Comput. Biol. Medicine.
[23] L. Cox. Statistical Disclosure Limitation , 2006 .
[24] Jimeng Sun,et al. Generating Multi-label Discrete Patient Records using Generative Adversarial Networks , 2017, MLHC.
[25] Der-Chiang Li,et al. The attribute-trend-similarity method to improve learning performance for small datasets , 2017, Int. J. Prod. Res..
[26] Der-Chiang Li,et al. A tree-based-trend-diffusion prediction procedure for small sample sets in the early stages of manufacturing systems , 2012, Expert Syst. Appl..
[27] Carri Glide-Hurst,et al. Implementation of a novel algorithm for generating synthetic CT images from magnetic resonance imaging data sets for prostate cancer radiation therapy. , 2015, International journal of radiation oncology, biology, physics.
[28] Huang Chong-fu,et al. Principle of information diffusion , 1997 .
[29] O. Mangasarian,et al. Multisurface method of pattern separation for medical diagnosis applied to breast cytology. , 1990, Proceedings of the National Academy of Sciences of the United States of America.
[30] Der-Chiang Li,et al. A genetic algorithm-based virtual sample generation technique to improve small data set learning , 2014, Neurocomputing.
[31] O. Mangasarian,et al. Robust linear programming discrimination of two linearly inseparable sets , 1992 .
[32] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[33] Stef van Buuren,et al. Multiple imputation of discrete and continuous data by fully conditional specification , 2007 .
[34] Der-Chiang Li,et al. Using structure-based data transformation method to improve prediction accuracies for small data sets , 2012, Decis. Support Syst..
[35] Der-Chiang Li,et al. Using mega-trend-diffusion and artificial samples in small data set learning for early flexible manufacturing system scheduling knowledge , 2007, Comput. Oper. Res..
[36] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[37] Max A. Little,et al. Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection , 2007, Biomedical engineering online.
[38] Stef van Buuren,et al. MICE: Multivariate Imputation by Chained Equations in R , 2011 .
[39] Roberto Todeschini,et al. QSAR models for bioconcentration: is the increase in the complexity justified by more accurate predictions? , 2015, Chemosphere.
[40] O. Mangasarian,et al. Pattern Recognition Via Linear Programming: Theory and Application to Medical Diagnosis , 1989 .
[41] Performance Evaluation of Supervised Machine Learning Classifiers for Predicting Healthcare Operational Decisions , 2020 .
[42] Kudakwashe Dube,et al. Approach and Method for Generating Realistic Synthetic Electronic Healthcare Records for Secondary Use , 2013, FHIES.
[43] Taochun Wang,et al. An automatic sampling ratio detection method based on genetic algorithm for imbalanced data classification , 2021, Knowl. Based Syst..
[44] Yonghe Liu,et al. Improving interpolation-based oversampling for imbalanced data learning , 2020, Knowl. Based Syst..
[45] Claudio Moraga,et al. A diffusion-neural-network for learning from small samples , 2004, Int. J. Approx. Reason..
[46] Mark Kramer,et al. Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record , 2017, J. Am. Medical Informatics Assoc..