A survival ensemble of extreme learning machine
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[1] Andreas Ziegler,et al. ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R , 2015, 1508.04409.
[2] Senén Barro,et al. Do we need hundreds of classifiers to solve real world classification problems? , 2014, J. Mach. Learn. Res..
[3] Lei Jia,et al. Accurate Probabilistic Error Bound for Eigenvalues of Kernel Matrix , 2009, ACML.
[4] Chee Kheong Siew,et al. Extreme learning machine: Theory and applications , 2006, Neurocomputing.
[5] M LeBlanc,et al. A review of tree-based prognostic models. , 1995, Cancer treatment and research.
[6] K. Liestøl,et al. Survival analysis and neural nets. , 1994, Statistics in medicine.
[7] Ponnuthurai N. Suganthan,et al. Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article] , 2016, IEEE Computational Intelligence Magazine.
[8] Chih-Jen Lin,et al. A Practical Guide to Support Vector Classication , 2008 .
[9] Bin Nan,et al. Doubly Penalized Buckley–James Method for Survival Data with High‐Dimensional Covariates , 2008, Biometrics.
[10] David R. Cox,et al. Regression models and life tables (with discussion , 1972 .
[11] Hongming Zhou,et al. Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[12] Paulo J. G. Lisboa,et al. Time-to-event analysis with artificial neural networks: An integrated analytical and rule-based study for breast cancer , 2007, 2007 International Joint Conference on Neural Networks.
[13] Feilong Cao,et al. A study on effectiveness of extreme learning machine , 2011, Neurocomputing.
[14] Amaury Lendasse,et al. Ensemble delta test-extreme learning machine (DT-ELM) for regression , 2014, Neurocomputing.
[15] Elia Biganzoli,et al. Artificial neural network for the joint modelling of discrete cause-specific hazards , 2006, Artif. Intell. Medicine.
[16] G. Clark,et al. A practical application of neural network analysis for predicting outcome of individual breast cancer patients , 2005, Breast Cancer Research and Treatment.
[17] Paulo J. G. Lisboa,et al. A Bayesian neural network approach for modelling censored data with an application to prognosis after surgery for breast cancer , 2003, Artif. Intell. Medicine.
[18] E Biganzoli,et al. Feed forward neural networks for the analysis of censored survival data: a partial logistic regression approach. , 1998, Statistics in medicine.
[19] Raymond J. Mooney,et al. Constructing Diverse Classifier Ensembles using Artificial Training Examples , 2003, IJCAI.
[20] Yi Lu,et al. Dissimilarity based ensemble of extreme learning machine for gene expression data classification , 2014, Neurocomputing.
[21] P. Bühlmann,et al. Survival ensembles. , 2006, Biostatistics.
[22] Denis Larocque,et al. A review of survival trees , 2011 .
[23] Chee Kheong Siew,et al. Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.
[24] Han Wang,et al. Ensemble Based Extreme Learning Machine , 2010, IEEE Signal Processing Letters.
[25] Daniel B. Mark,et al. TUTORIAL IN BIOSTATISTICS MULTIVARIABLE PROGNOSTIC MODELS: ISSUES IN DEVELOPING MODELS, EVALUATING ASSUMPTIONS AND ADEQUACY, AND MEASURING AND REDUCING ERRORS , 1996 .
[26] Jiang Gui,et al. Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data , 2005, Bioinform..
[27] Trevor Hastie,et al. Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent. , 2011, Journal of statistical software.
[28] Hemant Ishwaran,et al. Random Survival Forests , 2008, Wiley StatsRef: Statistics Reference Online.
[29] Elia Biganzoli,et al. Selection of artificial neural network models for survival analysis with Genetic Algorithms , 2007, Comput. Stat. Data Anal..
[30] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[31] R. Tibshirani. The lasso method for variable selection in the Cox model. , 1997, Statistics in medicine.
[32] Tin Kam Ho,et al. The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[33] Olaf Mersmann,et al. Accurate Timing Functions , 2015 .
[34] Leo Breiman,et al. Classification and Regression Trees , 1984 .
[35] Janez Demsar,et al. Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..
[36] I. James,et al. Linear regression with censored data , 1979 .
[37] P. Lapuerta,et al. Comparison of the performance of neural network methods and Cox regression for censored survival data , 2000 .
[38] Wenbin Lu,et al. Boosting method for nonlinear transformation models with censored survival data. , 2008, Biostatistics.
[39] E. Kaplan,et al. Nonparametric Estimation from Incomplete Observations , 1958 .
[40] R. Polikar,et al. Ensemble based systems in decision making , 2006, IEEE Circuits and Systems Magazine.
[41] Thomas G. Dietterich. Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms , 1998, Neural Computation.
[42] M. Ebell. Artificial neural networks for predicting failure to survive following in-hospital cardiopulmonary resuscitation. , 1993, The Journal of family practice.
[43] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[44] M. Akritas,et al. NonpModelCheck: An R Package for Nonparametric Lack-of-Fit Testing and Variable Selection , 2017 .
[45] D Faraggi,et al. A neural network model for survival data. , 1995, Statistics in medicine.
[46] Qinghua Zheng,et al. Regularized Extreme Learning Machine , 2009, 2009 IEEE Symposium on Computational Intelligence and Data Mining.