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This challenge of imbalanced classes is more prevalent in big data scenario due to its high volume. This study deals with acknowledging a sampling solution based on cluster computing in handling class imbalance problems in the case of big data. The newly proposed approach hybrid sampling algorithm (HSA) is assessed using three popular classification algorithms namely, support vector machine, decision tree and k-nearest neighbor based on balanced accuracy and elapsed time. The results obtained from the experiment are considered promising with an efficiency gain of 42% in comparison to the traditional sampling solution synthetic minority oversampling technique (SMOTE). This work proves the effectiveness of the distribution and clustering principle in imbalanced big data scenarios. <\/jats:p>","DOI":"10.1142\/s2424922x21500054","type":"journal-article","created":{"date-parts":[[2021,8,19]],"date-time":"2021-08-19T08:35:37Z","timestamp":1629362137000},"source":"Crossref","is-referenced-by-count":2,"title":["A Novel Hybrid Sampling Algorithm for Solving Class Imbalance Problem in Big Data"],"prefix":"10.1142","volume":"13","author":[{"given":"Khyati","family":"Ahlawat","sequence":"first","affiliation":[{"name":"Indira Gandhi Delhi Technical University for Women, Kashmere Gate, Delhi 110006, India"}]},{"given":"Anuradha","family":"Chug","sequence":"additional","affiliation":[{"name":"University School of Information, Communication and Technology, Guru Gobind Singh Indraprastha University, Sector 16C, Dwarka, Delhi 110078, India"}]},{"given":"Amit Prakash","family":"Singh","sequence":"additional","affiliation":[{"name":"University School of Information, Communication and Technology, Guru Gobind Singh Indraprastha University, Sector 16C, Dwarka, Delhi 110078, India"}]}],"member":"219","published-online":{"date-parts":[[2021,8,18]]},"reference":[{"key":"S2424922X21500054BIB001","doi-asserted-by":"publisher","DOI":"10.1007\/s13198-019-00817-6"},{"key":"S2424922X21500054BIB002","doi-asserted-by":"publisher","DOI":"10.4018\/IJGHPC.2019070102"},{"key":"S2424922X21500054BIB003","doi-asserted-by":"publisher","DOI":"10.1016\/j.bdr.2015.04.001"},{"key":"S2424922X21500054BIB004","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2015.10.041"},{"key":"S2424922X21500054BIB005","doi-asserted-by":"publisher","DOI":"10.1504\/IJBDI.2017.085521"},{"key":"S2424922X21500054BIB006","doi-asserted-by":"publisher","DOI":"10.1136\/amiajnl-2012-001409"},{"key":"S2424922X21500054BIB007","doi-asserted-by":"publisher","DOI":"10.1016\/j.jprocont.2019.06.011"},{"key":"S2424922X21500054BIB008","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2018.07.035"},{"key":"S2424922X21500054BIB009","doi-asserted-by":"publisher","DOI":"10.1080\/18756891.2016.1180820"},{"key":"S2424922X21500054BIB010","doi-asserted-by":"publisher","DOI":"10.1007\/s40747-017-0037-9"},{"key":"S2424922X21500054BIB011","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijinfomgt.2014.10.007"},{"key":"S2424922X21500054BIB012","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.12.002"},{"key":"S2424922X21500054BIB014","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2015.04.108"},{"key":"S2424922X21500054BIB015","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2017.01.005"},{"key":"S2424922X21500054BIB016","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-014-3487-0"},{"key":"S2424922X21500054BIB017","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-381479-1.00008-3"},{"key":"S2424922X21500054BIB018","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0274-4"},{"key":"S2424922X21500054BIB019","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2014.2332453"},{"key":"S2424922X21500054BIB020","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2016.2606104"},{"key":"S2424922X21500054BIB021","doi-asserted-by":"publisher","DOI":"10.1007\/s13748-016-0094-0"},{"key":"S2424922X21500054BIB022","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-015-0032-1"},{"issue":"42","key":"S2424922X21500054BIB023","volume":"5","author":"Leevy J. 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