TSCBAS: A Novel Correlation Based Attribute Selection Method and Application on Telecommunications Churn Analysis
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Dosyalar
Tarih
2018
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Attribute 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 rho-correlation coefficient-based attribute selection (rho 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, rho-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.
Açıklama
International Conference on Artificial Intelligence and Data Processing (IDAP) -- SEP 28-30, 2018 -- Inonu Univ, Malatya, TURKEY
BASARSLAN, MUHAMMET SINAN/0000-0002-7996-9169
WOS: 000458717400211
BASARSLAN, MUHAMMET SINAN/0000-0002-7996-9169
WOS: 000458717400211
Anahtar Kelimeler
Attribute Selection, TSCBAS, Telecommunications, Churn
Kaynak
2018 International Conference On Artificial Intelligence And Data Processing (Idap)
WoS Q Değeri
N/A
