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dc.contributor.authorKayaalp, Fatih
dc.contributor.authorBaşarslan, Muhammet Sinan
dc.contributor.authorPolat, Kemal
dc.date.accessioned2019-02-19T08:11:01Z
dc.date.available2019-02-19T08:11:01Z
dc.date.issued2018
dc.identifier.citationKayaalp, F., Başarslan, M.S., Polat, K. (2018). TSCBAS: a novel correlation based attribute selection method and application on telecommunications churn analysis. In 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), (pp. 1-5). Malatya, Turkey: IEEE. http://dx.doi.org/10.1109/IDAP.2018.8620935en_US
dc.identifier.isbn9781538668788
dc.identifier.isbn9781538668795
dc.identifier.urihttp://dx.doi.org/10.1109/IDAP.2018.8620935
dc.identifier.urihttp://hdl.handle.net/11376/3350
dc.descriptionBaşarslan, Muhammet Sinan (Dogus Author) -- Conference full title: 2018 International Conference on Artificial Intelligence and Data Processing (IDAP); IEEE; Malatya; Turkey; 28 September 2018 through 30 September 2018.en_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 Rcorrelation coefficient-based attribute selection (RCBAS) and the ρ-correlation coefficient-based attribute selection (ρCBAS) 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, ρ-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.en_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAttribute Selectionen_US
dc.subjectTSCBASen_US
dc.subjectTelecommunicationsen_US
dc.subjectChurnen_US
dc.titleTSCBAS: a novel correlation based attribute selection method and application on telecommunications churn analysisen_US
dc.typeconferenceObjecten_US
dc.relation.journal2018 International Conference on Artificial Intelligence and Data Processing (IDAP)en_US
dc.contributor.departmentDoğuş Üniversitesi, Meslek Yüksekokulu, Bilgisayar Programcılığı Programıen_US
dc.contributor.authorIDTR186560en_US
dc.contributor.authorIDTR278088en_US
dc.contributor.authorIDTR37249en_US
dc.identifier.startpage1en_US
dc.identifier.endpage5en_US


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