Comparison of Machine Learning Algorithms and Oversampling Techniques for Urinary Toxicity Prediction After Prostate Cancer Radiotherapy
暂无分享,去创建一个
Renaud de Crevoisier | Eugenia Mylona | Clement Lebreton | Pierre Fontaine | Stéphane Supiot | Nicolas Magne | Gilles Créhange | Oscar Acosta
[1] P. Maingon,et al. 70 Gy versus 80 Gy in localized prostate cancer: 5-year results of GETUG 06 randomized trial. , 2011, International journal of radiation oncology, biology, physics.
[2] David J. Hand,et al. Measuring classifier performance: a coherent alternative to the area under the ROC curve , 2009, Machine Learning.
[3] Ajinkya More,et al. Survey of resampling techniques for improving classification performance in unbalanced datasets , 2016, ArXiv.
[4] J. Friedman. Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .
[5] Lei Dong,et al. Long-term results of the M. D. Anderson randomized dose-escalation trial for prostate cancer. , 2008, International journal of radiation oncology, biology, physics.
[6] Gianluca Bontempi,et al. When is Undersampling Effective in Unbalanced Classification Tasks? , 2015, ECML/PKDD.
[7] D. Ruppert. The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .
[8] Gustavo E. A. P. A. Batista,et al. A study of the behavior of several methods for balancing machine learning training data , 2004, SKDD.
[9] Trevor Hastie,et al. Additive Logistic Regression : a Statistical , 1998 .
[10] Robert Tibshirani,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.
[11] Bartosz Krawczyk,et al. Learning from imbalanced data: open challenges and future directions , 2016, Progress in Artificial Intelligence.
[12] H. Thierens,et al. Integrated models for the prediction of late genitourinary complaints after high-dose intensity modulated radiotherapy for prostate cancer: making informed decisions. , 2014, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[13] Francisco Herrera,et al. An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics , 2013, Inf. Sci..
[14] Hui Han,et al. Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning , 2005, ICIC.
[15] Sulin Pang,et al. C5.0 Classification Algorithm and Application on Individual Credit Evaluation of Banks , 2009 .
[16] Claudio Fiorino,et al. Predictive models of toxicity in external radiotherapy , 2009, Cancer.
[17] B. Efron. Bayes' Theorem in the 21st Century , 2013, Science.
[18] Haibo He,et al. ADASYN: Adaptive synthetic sampling approach for imbalanced learning , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).
[19] Andrea Bellincontro,et al. Partial least squares discriminant analysis: A dimensionality reduction method to classify hyperspectral data , 2018, 1806.09347.
[20] Dennis L. Wilson,et al. Asymptotic Properties of Nearest Neighbor Rules Using Edited Data , 1972, IEEE Trans. Syst. Man Cybern..
[21] G. Dumancas. Comparison of machine-learning techniques for handling multicollinearity in big data analytics and high-performance data mining , 2015 .
[22] T. Rosewall,et al. The relationship between external beam radiotherapy dose and chronic urinary dysfunction--a methodological critique. , 2010, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[23] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[24] M. Parmar,et al. Escalated-dose versus control-dose conformal radiotherapy for prostate cancer: long-term results from the MRC RT01 randomised controlled trial. , 2014, The Lancet. Oncology.
[25] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[26] J. Friedman. Regularized Discriminant Analysis , 1989 .
[27] José Hernández-Orallo,et al. An experimental comparison of performance measures for classification , 2009, Pattern Recognit. Lett..
[28] N. Magné,et al. Voxel-Based Analysis for Identification of Urethrovesical Subregions Predicting Urinary Toxicity After Prostate Cancer Radiation Therapy. , 2019, International journal of radiation oncology, biology, physics.