Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/24917
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dc.contributor.authorAmin, Adnanen_UK
dc.contributor.authorAnwar, Sajiden_UK
dc.contributor.authorAdnan, Awaisen_UK
dc.contributor.authorNawaz, Muhammaden_UK
dc.contributor.authorHoward, Newtonen_UK
dc.contributor.authorQadir, Junaiden_UK
dc.contributor.authorHawalah, Ahmad Y Aen_UK
dc.contributor.authorHussain, Amiren_UK
dc.date.accessioned2017-08-26T08:54:44Z-
dc.date.available2017-08-26T08:54:44Z-
dc.date.issued2016-10-26en_UK
dc.identifier.urihttp://hdl.handle.net/1893/24917-
dc.description.abstractCustomer retention is a major issue for various service-based organizations particularly telecom industry, wherein predictive models for observing the behavior of customers are one of the great instruments in customer retention process and inferring the future behavior of the customers. However, the performances of predictive models are greatly affected when the real-world data set is highly imbalanced. A data set is called imbalanced if the samples size from one class is very much smaller or larger than the other classes. The most commonly used technique is over/under sampling for handling the class-imbalance problem (CIP) in various domains. In this paper, we survey six well-known sampling techniques and compare the performances of these key techniques, i.e., mega-trend diffusion function (MTDF), synthetic minority oversampling technique, adaptive synthetic sampling approach, couples top-N reverse k-nearest neighbor, majority weighted minority oversampling technique, and immune centroids oversampling technique. Moreover, this paper also reveals the evaluation of four rules-generation algorithms (the learning from example module, version 2 (LEM2), covering, exhaustive, and genetic algorithms) using publicly available data sets. The empirical results demonstrate that the overall predictive performance of MTDF and rules-generation based on genetic algorithms performed the best as compared with the rest of the evaluated oversampling methods and rule-generation algorithms.en_UK
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.relationAmin A, Anwar S, Adnan A, Nawaz M, Howard N, Qadir J, Hawalah AYA & Hussain A (2016) Comparing Oversampling Techniques to Handle the Class Imbalance Problem: A Customer Churn Prediction Case Study. IEEE Access, 4, pp. 7940-7957. https://doi.org/10.1109/access.2016.2619719en_UK
dc.rightsCopyright 2016 IEEE. IEEE's Open Access Publishing Agreement allows: OA authors are assured that they are free to post the final, published version of their articles on their personal websites, their employers' sites, or their funding agency's sites. The OAPA allows users to copy the work, as well as to translate it or to reuse it for text/data mining, as long as the usage is for non-commercial purposes. IEEE authors who want to submit their manuscripts under an OA license are encouraged to use the IEEE OAPA. Available at: http://www.ieee.org/publications_standards/publications/rights/oa_author_choices.htmlen_UK
dc.titleComparing Oversampling Techniques to Handle the Class Imbalance Problem: A Customer Churn Prediction Case Studyen_UK
dc.typeJournal Articleen_UK
dc.identifier.doi10.1109/access.2016.2619719en_UK
dc.citation.jtitleIEEE Accessen_UK
dc.citation.issn2169-3536en_UK
dc.citation.volume4en_UK
dc.citation.spage7940en_UK
dc.citation.epage7957en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.contributor.funderEngineering and Physical Sciences Research Councilen_UK
dc.author.emailahu@cs.stir.ac.uken_UK
dc.citation.date26/10/2016en_UK
dc.contributor.affiliationInstitute of Management Sciencesen_UK
dc.contributor.affiliationInstitute of Management Sciencesen_UK
dc.contributor.affiliationInstitute of Management Sciencesen_UK
dc.contributor.affiliationInstitute of Management Sciencesen_UK
dc.contributor.affiliationUniversity of Oxforden_UK
dc.contributor.affiliationInformation Technology Universityen_UK
dc.contributor.affiliationTaibah Universityen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.identifier.isiWOS:000390567700023en_UK
dc.identifier.scopusid2-s2.0-85012886203en_UK
dc.identifier.wtid536754en_UK
dc.contributor.orcid0000-0002-8080-082Xen_UK
dc.date.accepted2016-10-01en_UK
dcterms.dateAccepted2016-10-01en_UK
dc.date.filedepositdate2017-02-01en_UK
dc.relation.funderprojectTowards visually-driven speech enhancement for cognitively-inspired multi-modal hearing-aid devicesen_UK
dc.relation.funderrefEP/M026981/1en_UK
rioxxterms.apcnot requireden_UK
rioxxterms.typeJournal Article/Reviewen_UK
rioxxterms.versionVoRen_UK
local.rioxx.authorAmin, Adnan|en_UK
local.rioxx.authorAnwar, Sajid|en_UK
local.rioxx.authorAdnan, Awais|en_UK
local.rioxx.authorNawaz, Muhammad|en_UK
local.rioxx.authorHoward, Newton|en_UK
local.rioxx.authorQadir, Junaid|en_UK
local.rioxx.authorHawalah, Ahmad Y A|en_UK
local.rioxx.authorHussain, Amir|0000-0002-8080-082Xen_UK
local.rioxx.projectEP/M026981/1|Engineering and Physical Sciences Research Council|http://dx.doi.org/10.13039/501100000266en_UK
local.rioxx.freetoreaddate2017-02-01en_UK
local.rioxx.licencehttp://www.rioxx.net/licenses/all-rights-reserved|2017-02-01|en_UK
local.rioxx.filename07707454.pdfen_UK
local.rioxx.filecount1en_UK
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