EvoImputer: An evolutionary approach for Missing Data Imputation and feature selection in the context of supervised learning

[1]  Mohamad Faiz Dzulkalnine,et al.  Missing Data Imputation with Hybrid Feature Selection for Fertility Dataset , 2020, ASM Science Journal.

[2]  Min-Wei Huang,et al.  The Feature Selection Effect on Missing Value Imputation of Medical Datasets , 2020, Applied Sciences.

[3]  Bain Khusnul Khotimah,et al.  Optimization of Feature Selection Using Genetic Algorithm in Naïve Bayes Classification for Incomplete Data , 2020, International Journal of Intelligent Engineering and Systems.

[4]  Roselina Sallehuddin,et al.  Missing data imputation with fuzzy feature selection for diabetes dataset , 2019, SN Applied Sciences.

[5]  Negin Daneshpour,et al.  Missing value imputation using a novel grey based fuzzy c-means, mutual information based feature selection, and regression model , 2019, Expert Syst. Appl..

[6]  Vanathi Gopalakrishnan,et al.  An Overview and Evaluation of Recent Machine Learning Imputation Methods Using Cardiac Imaging Data , 2017, Data.

[7]  Fei Tang,et al.  Random forest missing data algorithms , 2017, Stat. Anal. Data Min..

[8]  Ejaz Ahmed,et al.  Missing Data Imputation using Genetic Algorithm for Supervised Learning , 2017 .

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

[10]  Fábio M. F. Lobato,et al.  An Evolutionary Missing Data Imputation Method for Pattern Classification , 2015, GECCO.

[11]  Vadlamani Ravi,et al.  Data imputation via evolutionary computation, clustering and a neural network , 2015, Neurocomputing.

[12]  Vadlamani Ravi,et al.  A new online data imputation method based on general regression auto associative neural network , 2014, Neurocomputing.

[13]  Paul Lodder,et al.  To Impute or not Impute, That’s the Question , 2014 .

[14]  Ferat Sahin,et al.  A survey on feature selection methods , 2014, Comput. Electr. Eng..

[15]  Ahmet Arslan,et al.  A hybrid method for imputation of missing values using optimized fuzzy c-means with support vector regression and a genetic algorithm , 2013, Inf. Sci..

[16]  Hyun Kang The prevention and handling of the missing data , 2013, Korean journal of anesthesiology.

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

[18]  Juan Carlos Figueroa García,et al.  Missing data imputation in multivariate data by evolutionary algorithms , 2011, Comput. Hum. Behav..

[19]  Esther-Lydia Silva-Ramírez,et al.  Missing value imputation on missing completely at random data using multilayer perceptrons , 2011, Neural Networks.

[20]  Amaury Lendasse,et al.  X-SOM and L-SOM: A double classification approach for missing value imputation , 2010, Neurocomputing.

[21]  Panos Liatsis,et al.  A robust missing value imputation method for noisy data , 2010, Applied Intelligence.

[22]  P. Fayers,et al.  Investigating the missing data mechanism in quality of life outcomes: a comparison of approaches , 2009, Health and quality of life outcomes.

[23]  Michel Verleysen,et al.  K nearest neighbours with mutual information for simultaneous classification and missing data imputation , 2009, Neurocomputing.

[24]  J. Graham,et al.  Missing data analysis: making it work in the real world. , 2009, Annual review of psychology.

[25]  T. Stijnen,et al.  Review: a gentle introduction to imputation of missing values. , 2006, Journal of clinical epidemiology.

[26]  Leonardo Franco,et al.  Missing data imputation in breast cancer prognosis , 2006 .

[27]  Tshilidzi Marwala,et al.  The use of genetic algorithms and neural networks to approximate missing data in database , 2005, IEEE 3rd International Conference on Computational Cybernetics, 2005. ICCC 2005..

[28]  Peter K. Sharpe,et al.  Dealing with missing values in neural network-based diagnostic systems , 1995, Neural Computing & Applications.

[29]  Pedro M. Domingos,et al.  On the Optimality of the Simple Bayesian Classifier under Zero-One Loss , 1997, Machine Learning.

[30]  D. Kibler,et al.  Instance-based learning algorithms , 2004, Machine Learning.

[31]  Lucila Ohno-Machado,et al.  Logistic regression and artificial neural network classification models: a methodology review , 2002, J. Biomed. Informatics.

[32]  Bogdan Gabrys,et al.  Neuro-fuzzy approach to processing inputs with missing values in pattern recognition problems , 2002, Int. J. Approx. Reason..

[33]  Gustavo E. A. P. A. Batista,et al.  A Study of K-Nearest Neighbour as an Imputation Method , 2002, HIS.

[34]  H. Stern,et al.  The use of multiple imputation for the analysis of missing data. , 2001, Psychological methods.

[35]  D. Bennett How can I deal with missing data in my study? , 2001, Australian and New Zealand journal of public health.

[36]  Amit Gupta,et al.  Estimating Missing Values Using Neural Networks , 1996 .

[37]  S. Nordbotten Neural network imputation applied to the Norwegian 1990 population census data , 1996 .

[38]  Darrell Whitley,et al.  A genetic algorithm tutorial , 1994, Statistics and Computing.

[39]  R. Little Regression with Missing X's: A Review , 2011 .

[40]  N M Laird,et al.  Missing data in longitudinal studies. , 1988, Statistics in medicine.

[41]  Wayne S. DeSarbo,et al.  A Constrained Unfolding Methodology for Product Positioning , 1986 .

[42]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .