{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T03:13:08Z","timestamp":1773976388822,"version":"3.50.1"},"reference-count":72,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100006565","name":"University of Johannesburg, South Africa","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100006565","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.3038658","type":"journal-article","created":{"date-parts":[[2020,11,17]],"date-time":"2020-11-17T21:44:15Z","timestamp":1605649455000},"page":"218015-218036","source":"Crossref","is-referenced-by-count":19,"title":["Empirical Comparison of Approaches for Mitigating Effects of Class Imbalances in Water Quality Anomaly Detection"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6125-0180","authenticated-orcid":false,"given":"Eustace M.","family":"Dogo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2607-7439","authenticated-orcid":false,"given":"Nnamdi I.","family":"Nwulu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3452-9581","authenticated-orcid":false,"given":"Bhekisipho","family":"Twala","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Clinton Ohis","family":"Aigbavboa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref72","article-title":"DESlib: A dynamic ensemble selection library in Python","author":"cruz","year":"2018","journal-title":"arXiv 1802 04967"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/ICTAI.2007.46"},{"key":"ref70","article-title":"Deep learning: A critical appraisal","author":"marcus","year":"2018","journal-title":"arXiv 1801 00631"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/1007730.1007735"},{"key":"ref38","first-page":"10","article-title":"Balancing training data for automated annotation of keywords: A case study","author":"batista","year":"2003","journal-title":"Proc WOBS"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1960.tb00375.x"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1977.tb01600.x"},{"key":"ref31","author":"little","year":"2019","journal-title":"Statistical Analysis with Missing Data"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btr597"},{"key":"ref37","first-page":"1322","article-title":"ADASYN: Adaptive synthetic sampling approach for imbalanced learning","author":"he","year":"2008","journal-title":"Proc IEEE Int Joint Conf Neural Netw (IEEE World Congr Comput Intell )"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"},{"key":"ref35","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1109\/TSMC.1976.4309523","article-title":"An experiment with the edited nearest-neighbor rule","volume":"smc 6","author":"tomek","year":"1976","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"ref34","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1109\/TSMC.1976.4309452","article-title":"Two modifications of CNN","volume":"smc 6","author":"tomek","year":"1976","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.4018\/IJMCMC.2014100102"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1007\/s10044-016-0553-z"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-13059-5_22"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/1895076"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.3390\/s20092625"},{"key":"ref64","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","author":"dem\u0161ar","year":"2006","journal-title":"J Mach Learn Res"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-29035-1_43"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511804090"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2018.8622396"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2014.12.073"},{"key":"ref67","author":"qi","year":"2020","journal-title":"Paleo A performance model for deep neural networks"},{"key":"ref68","first-page":"1579","article-title":"Fast kernel classifiers with online and active learning","volume":"6","author":"bordes","year":"2005","journal-title":"J Mach Learn Res"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.5120\/ijca2015906480"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1080\/1573062X.2019.1637002"},{"key":"ref1","author":"rehbach","year":"2018","journal-title":"Gecco 2018 industrial challenge Monitoring of drinking water quality"},{"key":"ref20","first-page":"1137","article-title":"A study of cross-validation and bootstrap for accuracy estimation and model selection","volume":"14","author":"kohavi","year":"1995","journal-title":"IJCAI"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/AFRCON.2013.6757711"},{"key":"ref21","author":"duda","year":"2016","journal-title":"Pattern Classification"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCC.2011.2161285"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1080\/08839510902872223"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-16621-2_13"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/2020390.2020394"},{"key":"ref50","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1109\/TSMCB.2008.2007853","article-title":"Exploratory undersampling for class-imbalance learning","volume":"39","author":"liu","year":"2009","journal-title":"IEEE Trans Syst Man Cybern B Cybern"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCA.2009.2029559"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2008.08.010"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1145\/2907070"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(05)80023-1"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.21105\/joss.00638"},{"key":"ref55","first-page":"3146","article-title":"LightGBM: A highly efficient gradient boosting decision tree","author":"ke","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref54","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/34.709601","article-title":"The random subspace method for constructing decision forests","volume":"20","author":"ho","year":"1998","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1007\/BF00058655"},{"key":"ref52","author":"chao","year":"2004","journal-title":"Using random forest to learn imbalanced data"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/s13748-016-0094-0"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.01.060"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-817084-7.00033-4"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.12.035"},{"key":"ref13","first-page":"1","article-title":"A comparison of six methods for missing data imputation","volume":"6","author":"schmitt","year":"2015","journal-title":"Journal of Biometrics &amp; Biostatistics"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/IMITEC45504.2019.9015889"},{"key":"ref15","first-page":"429","article-title":"Applying tree ensemble to detect anomalies in real-world water composition dataset","volume":"11314","author":"nguyen","year":"2018","journal-title":"Intelligent Data Engineering and Automated Learning"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1080\/24751839.2019.1565653"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3205651.3208202"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/3319619.3326745"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.02.022"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1037\/1082-989X.7.2.147"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2008.239"},{"key":"ref6","first-page":"559","article-title":"Imbalanced-learn: A Python toolbox to tackle the curse of imbalanced datasets in machine learning","volume":"18","author":"lemaitre","year":"2017","journal-title":"J Mach Learn Res"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3310205"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-014-0794-3"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-009-0295-6"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/SACI.2013.6609011"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-182656"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/S0020-7373(87)80053-6"},{"key":"ref48","author":"goodfellow","year":"2016","journal-title":"Deep Learning"},{"key":"ref47","first-page":"148","article-title":"Experiments with a new boosting algorithm","volume":"96","author":"freund","year":"1996","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/130385.130401"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/BF00153759"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1142\/S0218001405003983"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2005.12.001"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09261464.pdf?arnumber=9261464","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:55:02Z","timestamp":1639770902000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9261464\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":72,"URL":"https:\/\/doi.org\/10.1109\/access.2020.3038658","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}