{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:27:30Z","timestamp":1777696050903,"version":"3.51.4"},"reference-count":38,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDA"],"published-print":{"date-parts":[[2021,7,9]]},"abstract":"<jats:p>Almost all real-world datasets contain missing values. Classification of data with missing values can adversely affect the performance of a classifier if not handled correctly. A common approach used for classification with incomplete data is imputation. Imputation transforms incomplete data with missing values to complete data. Single imputation methods are mostly less accurate than multiple imputation methods which are often computationally much more expensive. This study proposes an imputed feature selected bagging (IFBag) method which uses multiple imputation, feature selection and bagging ensemble learning approach to construct a number of base classifiers to classify new incomplete instances without any need for imputation in testing phase. In bagging ensemble learning approach, data is resampled multiple times with substitution, which can lead to diversity in data thus resulting in more accurate classifiers. The experimental results show the proposed IFBag method is considerably fast and gives 97.26% accuracy for classification with incomplete data as compared to common methods used.<\/jats:p>","DOI":"10.3233\/ida-205331","type":"journal-article","created":{"date-parts":[[2021,7,13]],"date-time":"2021-07-13T14:25:47Z","timestamp":1626186347000},"page":"825-846","source":"Crossref","is-referenced-by-count":0,"title":["Handling incomplete data classification using imputed feature selected bagging (IFBag) method"],"prefix":"10.1177","volume":"25","author":[{"given":"Ahmad Jaffar","family":"Khan","sequence":"first","affiliation":[{"name":"Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Basit","family":"Raza","sequence":"additional","affiliation":[{"name":"Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmad Raza","family":"Shahid","sequence":"additional","affiliation":[{"name":"Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yogan Jaya","family":"Kumar","sequence":"additional","affiliation":[{"name":"Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad","family":"Faheem","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Abdullah Gul University, Kayseri, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hani","family":"Alquhayz","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information, College of Science in Zulfi, Majmaah University, Al-Majmaah, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/IDA-205331_ref1","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1016\/j.asoc.2018.09.026","article-title":"Improving performance of classification on incomplete data using feature selection and clustering","volume":"73","author":"Tran","year":"2018","journal-title":"Applied Soft Computing"},{"key":"10.3233\/IDA-205331_ref2","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.neucom.2019.07.066","article-title":"Imputations of missing values using a tracking-removed autoencoder trained with incomplete data","volume":"366","author":"Lai","year":"2019","journal-title":"Neurocomputing"},{"key":"10.3233\/IDA-205331_ref3","unstructured":"D. Dua and C. Graff, Uci machine learning repository.irvine, ca: University of california, school of information and computer science., 2019. http:\/\/archive.ics.uci.edu\/m6 Accessed January 21, 2019."},{"key":"10.3233\/IDA-205331_ref4","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.knosys.2013.12.005","article-title":"Fimus: A framework for imputing missing values using co-appearance, correlation and similarity analysis","volume":"56","author":"Rahman","year":"2014","journal-title":"Knowledge-Based Systems"},{"key":"10.3233\/IDA-205331_ref5","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.knosys.2016.12.019","article-title":"Dynamic financial distress prediction with concept drift based on time weighting combined with adaboost support vector machine ensemble","volume":"120","author":"Sun","year":"2017","journal-title":"Knowledge-Based Systems"},{"issue":"6","key":"10.3233\/IDA-205331_ref6","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":"10.3233\/IDA-205331_ref7","doi-asserted-by":"crossref","unstructured":"R.J. Little and D.B. Rubin, Statistical analysis with missing data (Vol. 793), John Wiley & Sons, 2019.","DOI":"10.1002\/9781119482260"},{"issue":"1","key":"10.3233\/IDA-205331_ref8","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1007\/s13042-015-0354-5","article-title":"Information-decomposition-model-based missing value estimation for not missing at random dataset","volume":"9","author":"Liu","year":"2018","journal-title":"International Journal of Machine Learning and Cybernetics"},{"key":"10.3233\/IDA-205331_ref9","doi-asserted-by":"crossref","first-page":"106437","DOI":"10.1016\/j.asoc.2020.106437","article-title":"Incomplete data classification with view-based decision tree","volume":"94","author":"Huang","year":"2020","journal-title":"Applied Soft Computing"},{"key":"10.3233\/IDA-205331_ref10","doi-asserted-by":"crossref","first-page":"105122","DOI":"10.1016\/j.cmpb.2019.105122","article-title":"R-Ensembler: A greedy rough set based ensemble attribute selection algorithm with kNN imputation for classification of medical data","volume":"184","author":"Bania","year":"2020","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"10.3233\/IDA-205331_ref11","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.knosys.2018.06.012","article-title":"Handling missing values: A study of popular imputation packages in R","volume":"160","author":"Yadav","year":"2018","journal-title":"Knowledge-Based Systems"},{"key":"10.3233\/IDA-205331_ref12","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.knosys.2017.06.033","article-title":"Heuristically repopulated bayesian ant colony optimization for treating missing values in large databases","volume":"133","author":"Priya","year":"2017","journal-title":"Knowledge-Based Systems"},{"issue":"4","key":"10.3233\/IDA-205331_ref13","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1002\/sim.4067","article-title":"Multiple imputation using chained equations: issues and guidance for practice","volume":"30","author":"White","year":"2011","journal-title":"Statistics in Medicine"},{"issue":"11","key":"10.3233\/IDA-205331_ref14","doi-asserted-by":"crossref","first-page":"3817","DOI":"10.1016\/j.patcog.2010.05.028","article-title":"Learn++. mf: A random subspace approach for the missing feature problem","volume":"43","author":"Polikar","year":"2010","journal-title":"Pattern Recognition"},{"key":"10.3233\/IDA-205331_ref15","doi-asserted-by":"crossref","first-page":"113160","DOI":"10.1016\/j.eswa.2019.113160","article-title":"A-stacking and a-bagging: Adaptive versions of ensemble learning algorithms for spoof fingerprint detection","volume":"146","author":"Agarwal","year":"2020","journal-title":"Expert Systems with Applications"},{"issue":"5","key":"10.3233\/IDA-205331_ref16","doi-asserted-by":"crossref","first-page":"1513","DOI":"10.1007\/s13042-016-0524-0","article-title":"A selective neural network ensemble classification for incomplete data","volume":"8","author":"Yan","year":"2017","journal-title":"International Journal of Machine Learning and Cybernetics"},{"key":"10.3233\/IDA-205331_ref17","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.neucom.2017.11.077","article-title":"Feature selection in machine learning: A new perspective","volume":"300","author":"Cai","year":"2018","journal-title":"Neurocomputing"},{"key":"10.3233\/IDA-205331_ref18","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.neucom.2012.02.031","article-title":"Feature selection with missing data using mutual information estimators","volume":"90","author":"Doquire","year":"2012","journal-title":"Neurocomputing"},{"issue":"3","key":"10.3233\/IDA-205331_ref19","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/s12065-016-0141-6","article-title":"Improving performance for classification with incomplete data using wrapper-based feature selection","volume":"9","author":"Tran","year":"2016","journal-title":"Evolutionary Intelligence"},{"key":"10.3233\/IDA-205331_ref20","doi-asserted-by":"crossref","first-page":"100275","DOI":"10.1016\/j.imu.2019.100275","article-title":"Mice vs ppca: Missing data imputation in healthcare","volume":"17","author":"Hegde","year":"2019","journal-title":"Informatics in Medicine Unlocked"},{"key":"10.3233\/IDA-205331_ref21","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.eswa.2019.04.049","article-title":"Model selection to improve multiple imputation for handling high rate missingness in a water quality dataset","volume":"131","author":"Ratolojanahary","year":"2019","journal-title":"Expert Systems with Applications"},{"issue":"1","key":"10.3233\/IDA-205331_ref22","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.ejor.2019.06.042","article-title":"The case for the use of multiple imputation missing data methods in stochastic frontier analysis with illustration using English local highway data","volume":"280","author":"Stead","year":"2020","journal-title":"European Journal of Operational Research"},{"key":"10.3233\/IDA-205331_ref23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.knosys.2018.05.013","article-title":"An effective and efficient approach to classification with incomplete data","volume":"154","author":"Tran","year":"2018","journal-title":"Knowledge-Based Systems"},{"key":"10.3233\/IDA-205331_ref24","first-page":"1","article-title":"Bootstrapping and multiple imputation ensemble approaches for classification problems","author":"Khan","year":"2019","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"issue":"3","key":"10.3233\/IDA-205331_ref25","doi-asserted-by":"crossref","first-page":"299","DOI":"10.3233\/IDA-2010-0423","article-title":"Ensemble missing data techniques for software effort prediction","volume":"14","author":"Twala","year":"2010","journal-title":"Intelligent Data Analysis"},{"issue":"5","key":"10.3233\/IDA-205331_ref26","first-page":"110","article-title":"Assessment of internal validity of prognostic models through bootstrapping and multiple imputation of missing data","volume":"41","author":"Baneshi","year":"2012","journal-title":"Iranian Journal of Public Health"},{"key":"10.3233\/IDA-205331_ref27","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.ins.2015.03.018","article-title":"Tree-based prediction on incomplete data using imputation or surrogate decisions","volume":"311","author":"Valdiviezo","year":"2015","journal-title":"Information Sciences"},{"issue":"14","key":"10.3233\/IDA-205331_ref28","doi-asserted-by":"crossref","first-page":"2252","DOI":"10.1002\/sim.7654","article-title":"Bootstrap inference when using multiple imputation","volume":"37","author":"Schomaker","year":"2018","journal-title":"Statistics in Medicine"},{"issue":"3","key":"10.3233\/IDA-205331_ref29","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1504\/IJIDS.2009.027657","article-title":"Making an accurate classifier ensemble by voting on classifications from imputed learning sets","volume":"1","author":"Su","year":"2009","journal-title":"International Journal of Information and Decision Sciences"},{"issue":"1","key":"10.3233\/IDA-205331_ref30","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.artmed.2011.11.006","article-title":"A classifier ensemble approach for the missing feature problem","volume":"55","author":"Nanni","year":"2012","journal-title":"Artificial Intelligence in Medicine"},{"issue":"1","key":"10.3233\/IDA-205331_ref31","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.compeleceng.2013.11.024","article-title":"A survey on feature selection methods","volume":"40","author":"Chandrashekar","year":"2014","journal-title":"Computers & Electrical Engineering"},{"key":"10.3233\/IDA-205331_ref32","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.knosys.2017.10.028","article-title":"Differential evolution for filter feature selection based on information theory and feature ranking","volume":"140","author":"Hancer","year":"2018","journal-title":"Knowledge-Based Systems"},{"key":"10.3233\/IDA-205331_ref33","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.neucom.2015.05.105","article-title":"Mutual information criterion for feature selection from incomplete data","volume":"168","author":"Qian","year":"2015","journal-title":"Neurocomputing"},{"issue":"1","key":"10.3233\/IDA-205331_ref34","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1002\/mpr.329","article-title":"Multiple imputation by chained equations: what is it and how does it work","volume":"20","author":"Azur","year":"2011","journal-title":"International Journal of Methods in Psychiatric Research"},{"key":"10.3233\/IDA-205331_ref35","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.patrec.2018.04.007","article-title":"A ranking-based feature selection approach for handwritten character recognition","volume":"121","author":"Cilia","year":"2019","journal-title":"Pattern Recognition Letters"},{"issue":"3","key":"10.3233\/IDA-205331_ref36","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1007\/s10489-018-1305-0","article-title":"Feature selection based on conditional mutual information: minimum conditional relevance and minimum conditional redundancy","volume":"49","author":"Zhou","year":"2019","journal-title":"Applied Intelligence"},{"key":"10.3233\/IDA-205331_ref38","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.enbuild.2018.05.031","article-title":"Using an ensemble machine learning methodology-bagging to predict occupants' thermal comfort in buildings","volume":"173","author":"Wu","year":"2018","journal-title":"Energy and Buildings"},{"key":"10.3233\/IDA-205331_ref39","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","author":"Dem\u0161ar","year":"2006","journal-title":"Journal of Machine Learning Research"}],"container-title":["Intelligent Data Analysis"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/IDA-205331","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:19:08Z","timestamp":1777454348000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/IDA-205331"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,9]]},"references-count":38,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.3233\/ida-205331","relation":{},"ISSN":["1088-467X","1571-4128"],"issn-type":[{"value":"1088-467X","type":"print"},{"value":"1571-4128","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,9]]}}}