The Feature Selection Effect on Missing Value Imputation of Medical Datasets

[1]  Gustavo E. A. P. A. Batista,et al.  An analysis of four missing data treatment methods for supervised learning , 2003, Appl. Artif. Intell..

[2]  Peter Bühlmann,et al.  MissForest - non-parametric missing value imputation for mixed-type data , 2011, Bioinform..

[3]  O. Thas,et al.  EMLasso: logistic lasso with missing data , 2013, Statistics in medicine.

[4]  Tome Eftimov,et al.  MIGHT: Statistical Methodology for Missing-Data Imputation in Food Composition Databases , 2019, Applied Sciences.

[5]  Alan Olinsky,et al.  The comparative efficacy of imputation methods for missing data in structural equation modeling , 2003, Eur. J. Oper. Res..

[6]  Man Leung Wong,et al.  Learning Bayesian networks from incomplete databases using a novel evolutionary algorithm , 2008, Decis. Support Syst..

[7]  Yan Lin,et al.  Missing value imputation in high-dimensional phenomic data: imputable or not, and how? , 2014, BMC Bioinformatics.

[8]  Aníbal R. Figueiras-Vidal,et al.  Pattern classification with missing data: a review , 2010, Neural Computing and Applications.

[9]  Alexander Hapfelmeier,et al.  Variable selection by Random Forests using data with missing values , 2014, Comput. Stat. Data Anal..

[10]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[11]  Xiaofeng Zhu,et al.  Local and Global Structure Preservation for Robust Unsupervised Spectral Feature Selection , 2018, IEEE Transactions on Knowledge and Data Engineering.

[12]  Hyeran Byun,et al.  A Survey on Pattern Recognition Applications of Support Vector Machines , 2003, Int. J. Pattern Recognit. Artif. Intell..

[13]  Chih-Fong Tsai,et al.  Missing value imputation: a review and analysis of the literature (2006–2017) , 2019, Artificial Intelligence Review.

[14]  Coral Barbas,et al.  Missing value imputation strategies for metabolomics data , 2015, Electrophoresis.

[15]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[16]  Esther-Lydia Silva-Ramírez,et al.  Single imputation with multilayer perceptron and multiple imputation combining multilayer perceptron and k-nearest neighbours for monotone patterns , 2015, Appl. Soft Comput..

[17]  Stefan Van Aelst,et al.  Tree-based prediction on incomplete data using imputation or surrogate decisions , 2015, Inf. Sci..

[18]  Beatriz de la Iglesia,et al.  Evolutionary computation for feature selection in classification problems , 2013, WIREs Data Mining Knowl. Discov..

[19]  Johan A. K. Suykens,et al.  Handling missing values in support vector machine classifiers , 2005, Neural Networks.

[20]  Nikos Tsikriktsis,et al.  A review of techniques for treating missing data in OM survey research , 2005 .

[21]  Xiaofeng Zhu,et al.  Efficient kNN Classification With Different Numbers of Nearest Neighbors , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[22]  Tao Li,et al.  Recent advances in feature selection and its applications , 2017, Knowledge and Information Systems.

[23]  Larry J. Eshelman,et al.  A dynamic ensemble approach to robust classification in the presence of missing data , 2015, Machine Learning.

[24]  A Rogier T Donders,et al.  Imputation of missing values is superior to complete case analysis and the missing-indicator method in multivariable diagnostic research: a clinical example. , 2006, Journal of clinical epidemiology.

[25]  Zili Zhang,et al.  Missing Value Estimation for Mixed-Attribute Data Sets , 2011, IEEE Transactions on Knowledge and Data Engineering.

[26]  J. Schafer,et al.  Missing data: our view of the state of the art. , 2002, Psychological methods.

[27]  Xin Yao,et al.  A Survey on Evolutionary Computation Approaches to Feature Selection , 2016, IEEE Transactions on Evolutionary Computation.

[28]  K. Thangavel,et al.  Missing value imputation using unsupervised machine learning techniques , 2019, Soft Computing.

[29]  Durga Toshniwal,et al.  MOWM: Multiple Overlapping Window Method for RBF based missing value prediction on big data , 2019, Expert Syst. Appl..

[30]  Dong-Kyu Kim,et al.  Enhanced Application of Principal Component Analysis in Machine Learning for Imputation of Missing Traffic Data , 2019, Applied Sciences.

[31]  Ke Lu,et al.  Missing data imputation by K nearest neighbours based on grey relational structure and mutual information , 2015, Applied Intelligence.

[32]  Shichao Zhang,et al.  "Missing is useful": missing values in cost-sensitive decision trees , 2005, IEEE Transactions on Knowledge and Data Engineering.

[33]  Yang Feng,et al.  VARIABLE SELECTION AND PREDICTION WITH INCOMPLETE HIGH-DIMENSIONAL DATA. , 2016, The annals of applied statistics.

[34]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[35]  Michel Verleysen,et al.  Feature selection with missing data using mutual information estimators , 2012, Neurocomputing.

[36]  Russ B. Altman,et al.  Missing value estimation methods for DNA microarrays , 2001, Bioinform..

[37]  Benjamin Guedj,et al.  Pycobra: A Python Toolbox for Ensemble Learning and Visualisation , 2017, J. Mach. Learn. Res..

[38]  J. Carpenter,et al.  Practice of Epidemiology Comparison of Random Forest and Parametric Imputation Models for Imputing Missing Data Using MICE: A CALIBER Study , 2014 .

[39]  Zexuan Zhu,et al.  Wrapper–Filter Feature Selection Algorithm Using a Memetic Framework , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).