Data imputation via evolutionary computation, clustering and a neural network
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[1] Tshilidzi Marwala,et al. Partial imputation of unseen records to improve classification using a hybrid multi-layered artificial immune system and genetic algorithm , 2013, Appl. Soft Comput..
[2] Yoshua Bengio,et al. Deep Sparse Rectifier Neural Networks , 2011, AISTATS.
[3] Pilsung Kang,et al. Locally linear reconstruction based missing value imputation for supervised learning , 2013, Neurocomputing.
[4] Amit Gupta,et al. Estimating Missing Values Using Neural Networks , 1996 .
[5] Ignacio Olmeda,et al. Hybrid Classifiers for Financial Multicriteria Decision Making: The Case of Bankruptcy Prediction , 1997 .
[6] D. Harville. Matrix Algebra From a Statistician's Perspective , 1998 .
[7] Serpil Canbas,et al. Prediction of commercial bank failure via multivariate statistical analysis of financial structures: The Turkish case , 2005, Eur. J. Oper. Res..
[8] Vadlamani Ravi,et al. Soft computing based imputation and hybrid data and text mining: The case of predicting the severity of phishing alerts , 2012, Expert Syst. Appl..
[9] Amaury Lendasse,et al. X-SOM and L-SOM: A double classification approach for missing value imputation , 2010, Neurocomputing.
[10] Paredes Fierro,et al. Análisis multivariante de unos datos de ecotoxicología , 2017 .
[11] Nikola Kasabov,et al. Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS): On-line learning and Application for Time-Series Prediction , 2000 .
[12] Vadlamani Ravi,et al. Evolving clustering based data imputation , 2014, 2014 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014].
[13] M. Marseguerra,et al. The AutoAssociative Neural Network in signal analysis: II. Application to on-line monitoring of a simulated BWR component , 2005 .
[14] Vadlamani Ravi,et al. A Computational Intelligence Based Online Data Imputation Method: An Application For Banking , 2013, J. Inf. Process. Syst..
[15] Md Zahidul Islam,et al. Missing value imputation using decision trees and decision forests by splitting and merging records: Two novel techniques , 2013, Knowl. Based Syst..
[16] Vadlamani Ravi,et al. A Novel Soft Computing Hybrid for Data Imputation , 2022 .
[17] Vadlamani Ravi,et al. A new online data imputation method based on general regression auto associative neural network , 2014, Neurocomputing.
[18] Peter K. Sharpe,et al. Dealing with missing values in neural network-based diagnostic systems , 1995, Neural Computing & Applications.
[19] A. V. Olgac,et al. Performance Analysis of Various Activation Functions in Generalized MLP Architectures of Neural Networks , 2011 .
[20] Aníbal R. Figueiras-Vidal,et al. Classifying patterns with missing values using Multi-Task Learning perceptrons , 2013, Expert Syst. Appl..
[21] Fengzhan Tian,et al. A selective Bayes Classifier for classifying incomplete data based on gain ratio , 2008, Knowl. Based Syst..
[22] Juan Carlos Figueroa García,et al. Missing data imputation in multivariate data by evolutionary algorithms , 2011, Comput. Hum. Behav..
[23] Yoshua Bengio,et al. Série Scientifique Scientific Series Incorporating Second-order Functional Knowledge for Better Option Pricing Incorporating Second-order Functional Knowledge for Better Option Pricing , 2022 .
[24] Slobodan P. Simonovic,et al. Estimation of missing streamflow data using principles of chaos theory , 2002 .
[25] Joseph L Schafer,et al. Analysis of Incomplete Multivariate Data , 1997 .
[26] Esther-Lydia Silva-Ramírez,et al. Missing value imputation on missing completely at random data using multilayer perceptrons , 2011, Neural Networks.
[27] N. H. Timm. Applied Multivariate Analysis , 2002 .
[28] Jonathan N. Crook,et al. Credit Scoring and Its Applications , 2002, SIAM monographs on mathematical modeling and computation.
[29] Guang-Bin Huang,et al. Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).
[30] Peter C. Austin,et al. Bayesian modeling of missing data in clinical research , 2005, Comput. Stat. Data Anal..
[31] Bogdan Gabrys,et al. Neuro-fuzzy approach to processing inputs with missing values in pattern recognition problems , 2002, Int. J. Approx. Reason..
[32] Rex B. Kline,et al. Principles and Practice of Structural Equation Modeling , 1998 .
[33] Tshilidzi Marwala,et al. A dynamic programming approach to missing data estimation using neural networks , 2013, Inf. Sci..
[34] S. Nordbotten. Neural network imputation applied to the Norwegian 1990 population census data , 1996 .
[35] Alessandro G. Di Nuovo,et al. Missing data analysis with fuzzy C-Means: A study of its application in a psychological scenario , 2011, Expert Syst. Appl..
[36] Tariq Samad,et al. Self–organization with partial data , 1992 .
[37] F. Wilcoxon. Individual Comparisons by Ranking Methods , 1945 .
[38] Leonardo Franco,et al. Missing data imputation in breast cancer prognosis , 2006 .
[39] Benito E. Flores,et al. A pragmatic view of accuracy measurement in forecasting , 1986 .
[40] R. Eberhart,et al. Comparing inertia weights and constriction factors in particle swarm optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).
[41] 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..
[42] John O. Odiyo,et al. Filling of missing rainfall data in Luvuvhu River Catchment using artificial neural networks , 2011 .
[43] Qinbao Song,et al. A new imputation method for small software project data sets , 2007, J. Syst. Softw..
[44] Gustavo E. A. P. A. Batista,et al. A Study of K-Nearest Neighbour as an Imputation Method , 2002, HIS.
[45] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[46] Vadlamani Ravi,et al. Particle swarm optimization and covariance matrix based data imputation , 2013, 2013 IEEE International Conference on Computational Intelligence and Computing Research.
[47] Wayne S. DeSarbo,et al. A Constrained Unfolding Methodology for Product Positioning , 1986 .
[48] Shichao Zhang,et al. Noisy data elimination using mutual k-nearest neighbor for classification mining , 2012, J. Syst. Softw..
[49] Chee Kheong Siew,et al. Extreme learning machine: Theory and applications , 2006, Neurocomputing.
[50] M. Beynon,et al. Variable precision rough set theory and data discretisation: an application to corporate failure prediction , 2001 .
[51] L. L. Doove,et al. Recursive partitioning for missing data imputation in the presence of interaction effects , 2014, Comput. Stat. Data Anal..
[52] Bruno Crémilleux,et al. MVC - a preprocessing method to deal with missing values , 1999, Knowl. Based Syst..
[53] Gustavo E. A. P. A. Batista,et al. Experimental comparison pf K-NEAREST NEIGHBOUR and MEAN OR MODE imputation methods with the internal strategies used by C4.5 and CN2 to treat missing data , 2003 .
[54] Alan Agresti,et al. Categorical Data Analysis , 1991, International Encyclopedia of Statistical Science.
[55] Teresa B. Ludermir,et al. Comparison of new activation functions in neural network for forecasting financial time series , 2011, Neural Computing and Applications.
[56] T. Marwala,et al. Fault classification in structures with incomplete measured data using autoassociative neural networks and genetic algorithm , 2006 .
[57] Soo-Young Lee,et al. Training Algorithm with Incomplete Data for Feed-Forward Neural Networks , 1999, Neural Processing Letters.
[58] M. Marseguerra,et al. The autoassociative neural network in signal analysis: III. Enhancing the reliability of a NN with application to a BWR , 2006 .
[59] James Kennedy,et al. Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.
[60] 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..