{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T20:40:17Z","timestamp":1761597617629},"reference-count":97,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2015,12,1]],"date-time":"2015-12-01T00:00:00Z","timestamp":1448928000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Sciences"],"published-print":{"date-parts":[[2015,12]]},"DOI":"10.1016\/j.ins.2015.07.016","type":"journal-article","created":{"date-parts":[[2015,7,11]],"date-time":"2015-07-11T22:01:13Z","timestamp":1436652073000},"page":"288-299","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/http\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":25,"special_numbering":"C","title":["Counter propagation auto-associative neural network based data imputation"],"prefix":"10.1016","volume":"325","author":[{"given":"Chandan","family":"Gautam","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vadlamani","family":"Ravi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.ins.2015.07.016_bib0001","series-title":"IEEE 3rd International Conference on Computational Cybernetics","first-page":"207","article-title":"The use of genetic algorithms and neural networks to approximate missing data in database","volume":"vol. 3","author":"Abdella","year":"2005"},{"key":"10.1016\/j.ins.2015.07.016_bib0002","series-title":"Proceedings of the 7th International Conference on Data Mining (DMIN)","article-title":"A novel soft computing hybrid for data imputation","author":"Ankaiah","year":"2011"},{"key":"10.1016\/j.ins.2015.07.016_bib0003","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.chemolab.2006.01.009","article-title":"QSAR study of anti-HIV HEPT analogues based on multi-objective genetic programming and counter-propagation neural network","volume":"83","author":"Arakawa","year":"2006","journal-title":"Chemo. Intell. Lab. Syst."},{"issue":"3","key":"10.1016\/j.ins.2015.07.016_bib0004","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1016\/j.csda.2004.06.006","article-title":"Bayesian modeling of missing data in clinical research","volume":"49","author":"Austin","year":"2005","journal-title":"Comput. Statics Data Anal."},{"key":"10.1016\/j.ins.2015.07.016_bib0005","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.ins.2013.01.021","article-title":"A hybrid method for imputation of missing vlaues using optimized fuzzy c-means with support vector regression and a genetic algorithm","volume":"233","author":"Aydilek","year":"2013","journal-title":"Informat. Sci."},{"key":"10.1016\/j.ins.2015.07.016_bib0006","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.chemolab.2009.05.007","article-title":"The Kohonen and CP-ANN toolbox: a collection of MATLAB modules for self-organizing maps and counterpropagation artificial neural networks","volume":"98","author":"Ballabio","year":"2009","journal-title":"Chemo. Intell. Lab. Syst."},{"key":"10.1016\/j.ins.2015.07.016_bib0007","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.chemolab.2006.09.002","article-title":"Characterization of the traditional Cypriot spirit Zivania by means of counterpropagation artificial neural networks","volume":"87","author":"Ballabio","year":"2007","journal-title":"Chemo. Intell. Lab. Syst."},{"key":"10.1016\/j.ins.2015.07.016_bib0008","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.chemolab.2010.10.010","article-title":"Genetic algorithms for architecture optimisation of counter-propagation artificial neural networks","volume":"105","author":"Ballabio","year":"2011","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"10.1016\/j.ins.2015.07.016_bib0009","series-title":"Hybrid Intelligent Systems, Ser Front Artificial Intelligence Applications","first-page":"251","article-title":"A study of K-nearest neighbor as an imputation method","author":"Batista","year":"2002"},{"key":"10.1016\/j.ins.2015.07.016_bib0010","series-title":"Experimental Comparison of K-Nearest Neighbor and Mean or Mode Imputation Methods with the Internal Strategies used by C4.5 and CN2 to Treat Missing Data","author":"Batista","year":"2003"},{"key":"10.1016\/j.ins.2015.07.016_bib0011","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1016\/S0305-0483(01)00045-7","article-title":"Variable precision rough set theory and data discretisation: an application to corporate failure prediction","volume":"29","author":"Beynon","year":"2001","journal-title":"Omega"},{"key":"10.1016\/j.ins.2015.07.016_bib0012","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.chemolab.2004.04.011","article-title":"Multivariate data analysis in classification of vegetable oils characterized by the content of fatty acids","volume":"75","author":"Brodnjak-Von\u010dina","year":"2005","journal-title":"Chemom. Intell. Lab. Syst."},{"issue":"9","key":"10.1016\/j.ins.2015.07.016_bib0013","doi-asserted-by":"crossref","first-page":"1495","DOI":"10.1016\/j.automatica.2004.04.011","article-title":"Autoregressive spectral analysis when observations are missing","volume":"40","author":"Broersen","year":"2004","journal-title":"Automatica"},{"key":"10.1016\/j.ins.2015.07.016_bib0014","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1016\/j.ejor.2004.03.023","article-title":"Prediction of commercial bank failure via multivariate statistical analysis of financial structures: the Turkish case","volume":"166","author":"Canbas","year":"2005","journal-title":"Eur. J. Oper. Res."},{"key":"10.1016\/j.ins.2015.07.016_bib0015","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/S0734-189X(87)80014-2","article-title":"A massively parallel architecture for a self-organizing neural pattern recognition machine","volume":"37","author":"Carpenter","year":"1987","journal-title":"Comput. Verion. Graph. Image Process."},{"issue":"12","key":"10.1016\/j.ins.2015.07.016_bib0016","doi-asserted-by":"crossref","first-page":"7639","DOI":"10.1016\/j.eswa.2010.04.079","article-title":"Copyright authentication for images with a full counter-propagation neural network","volume":"37","author":"Chang","year":"2010","journal-title":"Expert Syst. Appl."},{"issue":"7","key":"10.1016\/j.ins.2015.07.016_bib0017","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1016\/j.knosys.2008.03.013","article-title":"A selective Bayes Classifier for classifying incomplete data based on gain ratio","volume":"21","author":"Chen","year":"2008","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.ins.2015.07.016_bib0018","series-title":"ESCA Tutorial and Workshop on the Auditory Basis of Speech Perception, Keele University","first-page":"15","article-title":"Recognising occluded speech","author":"Cooke","year":"1996"},{"issue":"1","key":"10.1016\/j.ins.2015.07.016_bib0019","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum-likelihood from incomplete data via the EM algorithm","volume":"39","author":"Dempster","year":"1977","journal-title":"J. Roy. Stat. Soc."},{"key":"10.1016\/j.ins.2015.07.016_bib0020","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/S0167-6911(82)80025-X","article-title":"Control problems of grey system","volume":"1","author":"Deng","year":"1982","journal-title":"Syst. Control Lett."},{"issue":"1","key":"10.1016\/j.ins.2015.07.016_bib0021","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1287\/mksc.5.1.1","article-title":"A constrained unfolding methodology for product positioning","volume":"5","author":"Desarbo","year":"1986","journal-title":"Market. Sci."},{"key":"10.1016\/j.ins.2015.07.016_bib0022","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.csda.2013.10.025","article-title":"Recursive partitioning for missing data imputation in the presence of interaction effects","volume":"72","author":"Doove","year":"2013","journal-title":"Comput. Stat. Data Anal."},{"key":"10.1016\/j.ins.2015.07.016_bib0023","doi-asserted-by":"crossref","first-page":"4461","DOI":"10.1016\/j.asoc.2013.08.005","article-title":"Partial imputation of unseen records to improve classification using a hybrid multi-layered artificial immune system and genetic algorithm","volume":"13","author":"Duma","year":"2013","journal-title":"Appl. Soft Comput."},{"issue":"1","key":"10.1016\/j.ins.2015.07.016_bib0024","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/S0022-1694(01)00513-3","article-title":"Estimation of missing stream flow data using the principles of chaos theory","volume":"255","author":"Elshorbagy","year":"2002","journal-title":"J. Hydrol."},{"key":"10.1016\/j.ins.2015.07.016_bib0025","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.annepidem.2013.10.007","article-title":"Missing data in longitudinal studies: cross-sectional multiple imputation provides similar estimates to full-information maximum likelihood","volume":"24","author":"Ferro","year":"2014","journal-title":"Ann. Epidemiol."},{"issue":"2","key":"10.1016\/j.ins.2015.07.016_bib0026","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/0305-0483(86)90013-7","article-title":"A pragmatic view of accuracy measurement in forecasting","volume":"14","author":"Flores","year":"1986","journal-title":"Omega"},{"key":"10.1016\/j.ins.2015.07.016_bib0027","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/S0888-613X(02)00070-1","article-title":"Neuro-fuzzy approach to processing inputs with missing values in pattern recognition problems","volume":"30","author":"Gabrys","year":"2002","journal-title":"Int. J. Approx. Reason."},{"key":"10.1016\/j.ins.2015.07.016_bib0028","doi-asserted-by":"crossref","first-page":"1468","DOI":"10.1016\/j.chb.2010.06.026","article-title":"Missing data imputation in multivariate data by evolutionary algorithms","volume":"27","author":"Garc\u00eda","year":"2011","journal-title":"Comput. Hum. Behav."},{"key":"10.1016\/j.ins.2015.07.016_bib0029","doi-asserted-by":"crossref","first-page":"1333","DOI":"10.1016\/j.eswa.2012.08.057","article-title":"Classifying patterns with missing values using multi-task learning perceptrons","volume":"40","author":"Garcia-Laencina","year":"2013","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.ins.2015.07.016_bib0030","series-title":"Proceedings of the 3rd International Conference on Circuits, Power and Computing Technologies (ICCPCT)","article-title":"Evolving clustering based data imputation","author":"Gautam","year":"2013"},{"key":"10.1016\/j.ins.2015.07.016_bib0031","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.neucom.2014.12.073","article-title":"Data imputation via evolutionary computation, clustering and a neural network","volume":"156","author":"Gautam","year":"2015","journal-title":"Neurocomputing"},{"key":"10.1016\/j.ins.2015.07.016_bib0032","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.chemolab.2014.02.007","article-title":"A practical comparison of single and multiple imputation methods to handle complex missing data in air quality datasets","volume":"134","author":"G\u00f3mez-Carracedo","year":"2014","journal-title":"Chemom. Intell. Lab. Syst."},{"issue":"2","key":"10.1016\/j.ins.2015.07.016_bib0033","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1057\/jors.1996.21","article-title":"Estimating missing values using neural networks","volume":"47","author":"Gupta","year":"1996","journal-title":"J. Oper. Res. Soc."},{"key":"10.1016\/j.ins.2015.07.016_bib0034","doi-asserted-by":"crossref","first-page":"4979","DOI":"10.1364\/AO.26.004979","article-title":"Counterpropagation networks","volume":"26","author":"Hecht-Nielsen","year":"1987","journal-title":"Appl. Opt."},{"key":"10.1016\/j.ins.2015.07.016_bib0035","doi-asserted-by":"crossref","first-page":"1368","DOI":"10.1016\/j.cageo.2005.12.008","article-title":"The problem of missing data in geoscience databases","volume":"32","author":"Henley","year":"2006","journal-title":"Comput. Geosci."},{"key":"10.1016\/j.ins.2015.07.016_bib0036","unstructured":"https:\/\/2.zoppoz.workers.dev:443\/http\/www.cis.hut.fi\/projects\/somtoolbox\/, 2015 (accessed 18.07.15)."},{"key":"10.1016\/j.ins.2015.07.016_bib0037","unstructured":"https:\/\/2.zoppoz.workers.dev:443\/http\/www.disat.unimib.it\/chm, 2014 (accessed 11.11.14)."},{"key":"10.1016\/j.ins.2015.07.016_bib0038","unstructured":"E. Ramos, D. Donoho, Auto MPG dataset retrieved from https:\/\/2.zoppoz.workers.dev:443\/http\/archive.ics.uci.edu\/ml\/machine-learning-databases\/auto-mpg\/auto-mpg.data, StatLib library, Carnegie Mellon University, 2015 (accessed 18.07.15)."},{"key":"10.1016\/j.ins.2015.07.016_bib0039","unstructured":"P. Cortez, A. Morais, Forest Fire dataset retrieved from https:\/\/2.zoppoz.workers.dev:443\/http\/archive.ics.uci.edu\/ml\/machine-learning-databases\/forest-fires\/forestfires.csv, 2014 (accessed 11.11.14)."},{"key":"10.1016\/j.ins.2015.07.016_bib0040","unstructured":"D. Harrison, D.L. Rubinfeld, Boston Housing dataset retrieved from https:\/\/2.zoppoz.workers.dev:443\/http\/archive.ics.uci.edu\/ml\/machine-learning-databases\/housing\/housing.data, 2015 (accessed 18.07.15)."},{"key":"10.1016\/j.ins.2015.07.016_bib0041","unstructured":"R.A. Fisher, Iris dataset retrieved from https:\/\/2.zoppoz.workers.dev:443\/http\/archive.ics.uci.edu\/ml\/machine-learning-databases\/iris\/iris.data, 2015 (accessed 18.07.15)."},{"key":"10.1016\/j.ins.2015.07.016_bib0042","unstructured":"Owner of dataset: National Institute of Diabetes and Digestive and Kidney Diseases, Pima Indian Diabetes dataset retrieved from https:\/\/2.zoppoz.workers.dev:443\/http\/archive.ics.uci.edu\/ml\/datasets\/Pima+Indians+Diabetes, 2014 (accessed 11.11.14)."},{"key":"10.1016\/j.ins.2015.07.016_bib0043","unstructured":"K.J. Cios, L.A. Kurgan, L.S. Goodenday, Spectf Heart dataset retrieved from https:\/\/2.zoppoz.workers.dev:443\/http\/archive.ics.uci.edu\/ml\/machine-learning-databases\/spect, 2015 (accessed 18.07.15)."},{"key":"10.1016\/j.ins.2015.07.016_bib0044","unstructured":"Wine dataset retrieved from https:\/\/2.zoppoz.workers.dev:443\/http\/archive.ics.uci.edu\/ml\/machine-learning-databases\/wine\/wine.data, 2015 (accessed 18.07.15)."},{"key":"10.1016\/j.ins.2015.07.016_bib0045","unstructured":"R.W. Johnson, Bodyfat dataset retrieved from https:\/\/2.zoppoz.workers.dev:443\/http\/lib.stat.cmu.edu\/datasets\/bodyfat, StatLib library, Carnegie Mellon University, 2015 (accessed 18.07.15)."},{"key":"10.1016\/j.ins.2015.07.016_bib0046","unstructured":"Spanish dataset retrieved from https:\/\/2.zoppoz.workers.dev:443\/http\/www.tbb.org.tr\/english\/bulten\/yillik\/2000\/ratios.xls, 2015 (accessed 18.07.15)."},{"key":"10.1016\/j.ins.2015.07.016_bib0047","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1016\/j.ins.2010.12.017","article-title":"Incomplete-case nearest neighbor imputation in software measurement data","volume":"259","author":"Hulse","year":"2014","journal-title":"Inform. Sci."},{"key":"10.1016\/j.ins.2015.07.016_bib0048","series-title":"Proceedings of the 24th IASTED International Conference on Biomedical Engineering (BioMed\u201906), Anaheim, CA, USA","article-title":"Missing data imputation in breast cancer prognosis","author":"Jerez","year":"2006"},{"key":"10.1016\/j.ins.2015.07.016_bib0049","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.atmosenv.2014.11.049","article-title":"Imputation of missing data in time series for air pollutants","volume":"102","author":"Junger","year":"2015","journal-title":"Atmos. Environ."},{"key":"10.1016\/j.ins.2015.07.016_bib0050","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.neucom.2013.02.016","article-title":"Locally linear reconstruction based missing value imputation for supervised learning","volume":"118","author":"Kang","year":"2013","journal-title":"Neurocomputing"},{"key":"10.1016\/j.ins.2015.07.016_bib0051","series-title":"Principles and Practice of Structural Equation Modeling","author":"Kline","year":"1988"},{"key":"10.1016\/j.ins.2015.07.016_bib0052","series-title":"Self-Organization and Associative Memory","author":"Kohonen","year":"1988"},{"key":"10.1016\/j.ins.2015.07.016_bib0053","series-title":"IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Enathi","first-page":"1","article-title":"Particle swarm optimization and covariance matrix based data imputation","author":"Krishna","year":"2013"},{"key":"10.1016\/j.ins.2015.07.016_bib0054","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.chemolab.2007.07.003","article-title":"Counter-propagation neural networks in MATLAB","volume":"90","author":"Kuzmanovski","year":"2008","journal-title":"Chemo. Intell. Lab. Syst."},{"key":"10.1016\/j.ins.2015.07.016_bib0055","series-title":"IEEE Southeast Conference, Huntsville, Alabama","first-page":"533","article-title":"Classification with missing data in a wireless sensor network","author":"Li","year":"2008"},{"key":"10.1016\/j.ins.2015.07.016_bib0056","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.inffus.2012.08.007","article-title":"Nearest neighbour imputation using spatial\u2013temporal correlations in wireless sensor networks","volume":"15","author":"Li","year":"2014","journal-title":"Informat. Fusion"},{"issue":"5","key":"10.1016\/j.ins.2015.07.016_bib0057","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1016\/j.jss.2011.12.019","article-title":"Noisy data elimination using mutual k-nearest neighbor for classification mining","volume":"85","author":"Liu","year":"2012","journal-title":"J. Syst. Softw."},{"issue":"11","key":"10.1016\/j.ins.2015.07.016_bib0058","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1016\/j.anucene.2005.03.005","article-title":"The auto-associative neural network in signal analysis II. Application to on-line monitoring of a simulated BWR component","volume":"32","author":"Marseguerra","year":"2002","journal-title":"Ann. Nuclear Energy"},{"issue":"4","key":"10.1016\/j.ins.2015.07.016_bib0059","first-page":"542","article-title":"Fault classification in structures with incomplete measured data using auto associative neural networks and genetic algorithm","volume":"90","author":"Marwala","year":"2006","journal-title":"Current Sci. India"},{"key":"10.1016\/j.ins.2015.07.016_bib0060","unstructured":"MATLAB version 7.10.0. Natick, Massachusetts: The MathWorks Inc., 2010."},{"key":"10.1016\/j.ins.2015.07.016_bib0061","series-title":"Elements of Artificial Neural Networks","author":"Mehrotra","year":"1996"},{"key":"10.1016\/j.ins.2015.07.016_bib0062","doi-asserted-by":"crossref","first-page":"1103","DOI":"10.1016\/j.neucom.2009.11.019","article-title":"X-SOM and L-SOM: a double classification approach for missing value imputation","volume":"73","author":"Merlin","year":"2010","journal-title":"Neurocomputing"},{"key":"10.1016\/j.ins.2015.07.016_bib0063","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.ins.2009.10.008","article-title":"A dynamic programming approach to missing data estimation using neural networks","volume":"237","author":"Nelwamondo","year":"2013","journal-title":"Informat. Sci."},{"issue":"4","key":"10.1016\/j.ins.2015.07.016_bib0064","doi-asserted-by":"crossref","first-page":"633","DOI":"10.3745\/JIPS.2013.9.4.633","article-title":"A computational intelligence based online data imputation method: an application for banking","volume":"9","author":"Nishanth","year":"2013","journal-title":"J. Inform. Process. Syst."},{"issue":"12","key":"10.1016\/j.ins.2015.07.016_bib0065","doi-asserted-by":"crossref","first-page":"10583","DOI":"10.1016\/j.eswa.2012.02.138","article-title":"Soft computing based imputation and hybrid data and text mining: the case of predicting the severity of phishing alerts","volume":"39","author":"Nishanth","year":"2012","journal-title":"Expert Syst. Appl."},{"issue":"14\u201315","key":"10.1016\/j.ins.2015.07.016_bib0066","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1016\/j.pce.2011.07.041","article-title":"Filling of missing rainfall data in Luvuvhu river catchment using artificial neural networks","volume":"36","author":"Nkuna","year":"2011","journal-title":"Phys. Chem. Earth A\/B\/C"},{"key":"10.1016\/j.ins.2015.07.016_bib0067","first-page":"385","article-title":"Neural network imputation applied to the Norwegian 1990 population census data","volume":"12","author":"Nordbotten","year":"1996","journal-title":"J. Off. Stat."},{"issue":"6","key":"10.1016\/j.ins.2015.07.016_bib0068","doi-asserted-by":"crossref","first-page":"6793","DOI":"10.1016\/j.eswa.2010.12.067","article-title":"Missing data analysis with fuzzy C-Means: A study of its application in a psychological scenario","volume":"38","author":"Nuovo","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.ins.2015.07.016_bib0069","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1023\/A:1008668718837","article-title":"Hybrid classifiers for financial multicriteria decision making: the case of bankruptcy prediction","volume":"10","author":"Olmeda","year":"1997","journal-title":"Comput. Econom."},{"key":"10.1016\/j.ins.2015.07.016_bib0070","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.spl.2014.12.006","article-title":"A kernel-assisted imputation estimating method for the additive hazards model with missing censoring indicator","volume":"98","author":"Qiu","year":"2015","journal-title":"Stat. Probab. Lett."},{"key":"10.1016\/j.ins.2015.07.016_bib0071","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/S0950-7051(99)00022-2","article-title":"MVC\u2014a preprocessing method to deal with missing values","volume":"12","author":"Ragel","year":"1999","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.ins.2015.07.016_bib0072","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.knosys.2013.08.023","article-title":"Missing value imputation using decision trees and decision forests by splitting and merging records: two novel techniques","volume":"53","author":"Rahman","year":"2013","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.ins.2015.07.016_bib0073","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/S0169-7439(02)00110-7","article-title":"Separation of data on the training and test set for modelling: a case study for modelling of five colour properties of a white pigment","volume":"65","author":"Rajer-Kandu\u010d","year":"2003","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"10.1016\/j.ins.2015.07.016_bib0074","first-page":"89","article-title":"Bayesian network data imputation with application to survival tree analysis","volume":"98","author":"Rancoita","year":"2015","journal-title":"Comput. Stat. Data Anal."},{"key":"10.1016\/j.ins.2015.07.016_bib0075","series-title":"Encyclopaedia of Health Economics","first-page":"292","article-title":"Missing data: weighting and imputation","author":"Rathouz","year":"2014"},{"key":"10.1016\/j.ins.2015.07.016_bib0076","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.neucom.2014.02.037","article-title":"A new online data imputation method based on general regression auto associative neural network","volume":"138","author":"Ravi","year":"2014","journal-title":"Neurocomputing"},{"key":"10.1016\/j.ins.2015.07.016_bib0077","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1088\/0954-898X_3_2_008","article-title":"Self-organization with partial data network","volume":"3","author":"Samad","year":"1992","journal-title":"Comput. Neural Syst."},{"key":"10.1016\/j.ins.2015.07.016_bib0078","series-title":"Analysis of Incomplete Multivariate Data","author":"Schafer","year":"1997"},{"issue":"16","key":"10.1016\/j.ins.2015.07.016_bib0079","doi-asserted-by":"crossref","first-page":"3187","DOI":"10.1029\/2000GL012698","article-title":"Singular spectrum analysis for time series with missing data","volume":"28","author":"Schoellhamer","year":"2001","journal-title":"Geophys. Res. Lett."},{"issue":"2","key":"10.1016\/j.ins.2015.07.016_bib0080","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/BF01421959","article-title":"Dealing with missing values in neural network based diagnostic systems","volume":"3","author":"Sharpe","year":"1995","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.ins.2015.07.016_bib0081","series-title":"Non-Parametric Statistics for the Behavioral Sciences","first-page":"75","author":"Siegel","year":"1956"},{"issue":"1","key":"10.1016\/j.ins.2015.07.016_bib0082","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.neunet.2010.09.008","article-title":"Missing value imputation on missing completely at random data using multilayer perceptrons","volume":"24","author":"Silva-Ram\u00edrez","year":"2011","journal-title":"Neural Netw."},{"key":"10.1016\/j.ins.2015.07.016_bib0083","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.asoc.2014.09.052","article-title":"Single imputation with multilayer perceptron and multiple imputation combining multilayer perceptron and k-nearest neighbours for monotone patterns","volume":"29","author":"Silva-Ram\u00edrez","year":"2015","journal-title":"Appl. Soft Comput."},{"issue":"1","key":"10.1016\/j.ins.2015.07.016_bib0084","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.jss.2006.05.003","article-title":"A new imputation method for small software project data sets","volume":"80","author":"Song","year":"2007","journal-title":"J. Syst. Software"},{"key":"10.1016\/j.ins.2015.07.016_bib0085","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.trc.2014.11.003","article-title":"A hybrid approach to integrate fuzzy C-means based imputation method with genetic algorithm for missing traffic volume data estimation","volume":"51","author":"Tang","year":"2015","journal-title":"Transp. Res. Part C: Emerg. Technol."},{"key":"10.1016\/j.ins.2015.07.016_bib0086","series-title":"SIAM, Philadelphia","article-title":"Credit Scoring and its Applications","author":"Thomas","year":"2002"},{"key":"10.1016\/j.ins.2015.07.016_bib0087","first-page":"1","article-title":"\"Missing data analyses: a hybrid multiple imputation algorithm using grey system theory and entropy based on clustering","volume":"40","author":"Tian","year":"2013","journal-title":"Appl. Intell."},{"key":"10.1016\/j.ins.2015.07.016_bib0088","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1093\/bioinformatics\/17.6.520","article-title":"Missing value estimation methods for DNA microarrays","volume":"17","author":"Troyanskaya","year":"2001","journal-title":"Bioinformatics"},{"key":"10.1016\/j.ins.2015.07.016_bib0089","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/S0895-4356(01)00476-0","article-title":"Attrition in longitudinal studies: How to deal with missing data","volume":"55","author":"Twisk","year":"2002","journal-title":"J. Clinical Epidemiol."},{"key":"10.1016\/j.ins.2015.07.016_bib0090","doi-asserted-by":"crossref","first-page":"111","DOI":"10.3233\/IDA-1999-3203","article-title":"SOM-based data visualization methods","volume":"3","author":"Vesanto","year":"1999","journal-title":"Intell. Data Anal."},{"key":"10.1016\/j.ins.2015.07.016_bib0091","series-title":"SOM Toolbox for Matlab 5","author":"Vesanto","year":"2000"},{"key":"10.1016\/j.ins.2015.07.016_bib0092","doi-asserted-by":"crossref","first-page":"80","DOI":"10.2307\/3001968","article-title":"Individual comparisons by ranking methods","volume":"1","author":"Wilcoxon","year":"1945","journal-title":"Biometrics Bull"},{"key":"10.1016\/j.ins.2015.07.016_bib0093","unstructured":"Retrieved from www.sussex.ac.uk\/Users\/grahamh\/RM1web\/WilcoxonTable2005.pdf, 2015 (accessed 18.07.15)."},{"key":"10.1016\/j.ins.2015.07.016_bib0094","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1023\/A:1018772122605","article-title":"Training algorithm with incomplete data for feed-forward neural networks","volume":"10","author":"Yoon","year":"1999","journal-title":"Neural Process. Lett."},{"issue":"11","key":"10.1016\/j.ins.2015.07.016_bib0095","doi-asserted-by":"crossref","first-page":"2541","DOI":"10.1016\/j.jss.2012.05.073","article-title":"Nearest neighbor selection for iteratively kNN imputation","volume":"85","author":"Zhang","year":"2012","journal-title":"J. Syst. Softw."},{"key":"10.1016\/j.ins.2015.07.016_bib0096","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0169-7439(97)00030-0","article-title":"Kohonen and counterpropagation artificial neural networks in analytical chemistry","volume":"38","author":"Zupan","year":"1997","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"10.1016\/j.ins.2015.07.016_bib0097","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/0169-7439(95)80022-2","article-title":"Neural networks with counter-propagation learning strategy used for modelling","volume":"27","author":"Zupan","year":"1995","journal-title":"Chemom. Intell. Lab. Syst"}],"container-title":["Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/api.elsevier.com\/content\/article\/PII:S0020025515005083?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/api.elsevier.com\/content\/article\/PII:S0020025515005083?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,6,9]],"date-time":"2024-06-09T23:11:14Z","timestamp":1717974674000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/linkinghub.elsevier.com\/retrieve\/pii\/S0020025515005083"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,12]]},"references-count":97,"alternative-id":["S0020025515005083"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.ins.2015.07.016","relation":{},"ISSN":["0020-0255"],"issn-type":[{"value":"0020-0255","type":"print"}],"subject":[],"published":{"date-parts":[[2015,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Counter propagation auto-associative neural network based data imputation","name":"articletitle","label":"Article Title"},{"value":"Information Sciences","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.ins.2015.07.016","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"Copyright \u00a9 2015 Elsevier Inc. All rights reserved.","name":"copyright","label":"Copyright"}]}}