TSCBAS: A Novel Correlation Based Attribute Selection Method and Application on Telecommunications Churn Analysis

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dc.authorscopusid56495320500
dc.authorscopusid57203003458
dc.authorscopusid8945093900
dc.contributor.authorKayaalp, F.
dc.contributor.authorBaşarslan, M. S.
dc.contributor.authorPolat, K.
dc.date.accessioned2021-12-01T18:39:06Z
dc.date.available2021-12-01T18:39:06Z
dc.date.issued2019
dc.department[Belirlenecek]en_US
dc.description2018 International Conference on Artificial Intelligence and Data Processing, IDAP 2018 -- 28 September 2018 through 30 September 2018 -- -- 144523en_US
dc.description.abstractAttribute selection has a significant effect on the performance of the machine learning studies by selecting the attributes having significant effect on result, reducing the number of attributes, and reducing the calculation cost. In this study, a new attribute selection method which is a combination of the R-correlation coefficient-based attribute selection (RCBAS) and the ?-correlation coefficient-based attribute selection (?CBAS) called the Two-Stage Correlation-Based Attribute Selection (TSCBAS) is proposed to select significant attributes. The proposed attribute selection method has been applied to customer churn prediction on a telecommunications dataset for performance evaluation. The dataset used in the study includes real customer call records details for the years 2013 and 2014 obtained from a major telecommunications company in Turkey. Apart from the proposed attribute selection method, four different methods named Rcorrelation coefficient-based attribute selection, ?-correlation coefficient-based attribute selection, ReliefF, and Gain Ratio have been used for creating five datasets. After that, four classifier algorithms including Random Forest, C4.5 Decision Tree, Naive Bayes and AdaBoost.M1 have been applied. The obtained results have been compared according to the performance metrics comprising Accuracy (ACC), Sensitivity (TPR), Specificity (SPC), F-measure (F), AUC (area under the ROC curve), and run-time. The results of the comparisons show that the proposed attribute selection algorithm outperforms the state of the art methods on customer churn prediction. © 2018 IEEE.en_US
dc.identifier.doi10.1109/IDAP.2018.8620935
dc.identifier.isbn9781538668788
dc.identifier.scopus2-s2.0-85062503222en_US
dc.identifier.urihttps://doi.org/10.1109/IDAP.2018.8620935
dc.identifier.urihttps://hdl.handle.net/20.500.12684/10007
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2018 International Conference on Artificial Intelligence and Data Processing, IDAP 2018en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAttribute Selectionen_US
dc.subjectChurnen_US
dc.subjectTelecommunicationsen_US
dc.subjectTSCBASen_US
dc.titleTSCBAS: A Novel Correlation Based Attribute Selection Method and Application on Telecommunications Churn Analysisen_US
dc.typeConference Objecten_US

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