Sequential churn prediction and analysis of cellular network users-a multi-class, multi-label perspective

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dc.contributor.authorKhan, Farhanen_US
dc.contributor.authorKozat, Süleyman Serdaren_US
dc.coverage.spatialAntalya, Turkeyen_US
dc.date.accessioned2018-04-12T11:44:34Z
dc.date.available2018-04-12T11:44:34Z
dc.date.issued2017en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionDate of Conference: 15-18 May 2017en_US
dc.descriptionConference Name: IEEE 25th Signal Processing and Communications Applications Conference, SIU 2017en_US
dc.description.abstractWe investigate the problem of churn detection and prediction using sequential cellular network data. We introduce a cleaning and preprocessing of the dataset that makes it suitable for the analysis. We draw a comparison of the churn prediction results from the-state-of-the-art algorithms such as the Gradient Boosting Trees, Random Forests, basic Long Short-Term Memory (LSTM) and Support Vector Machines (SVM). We achieve significant performance boost by incorporating the sequential nature of the data, imputing missing information and analyzing the effects of various features. This in turns makes the classifier rigorous enough to give highly accurate results. We emphasize on the sequential nature of the problem and seek algorithms that can track the variations in the data. We test and compare the performance of proposed algorithms using performance measures on real life cellular network data for churn detection.en_US
dc.identifier.doi10.1109/SIU.2017.7960659en_US
dc.identifier.urihttp://hdl.handle.net/11693/37581
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/SIU.2017.7960659en_US
dc.source.titleProceedings of the IEEE 25th Signal Processing and Communications Applications Conference, SIU 2017en_US
dc.subjectChurnen_US
dc.subjectLSTMen_US
dc.subjectMulti-classen_US
dc.subjectSequential learningen_US
dc.subjectDecision treesen_US
dc.subjectLong short-term memoryen_US
dc.subjectMobile telecommunication systemsen_US
dc.subjectSupport vector machinesen_US
dc.subjectWireless networksen_US
dc.subjectPerformance measureen_US
dc.subjectState-of-the-art algorithmsen_US
dc.subjectSignal processingen_US
dc.titleSequential churn prediction and analysis of cellular network users-a multi-class, multi-label perspectiveen_US
dc.typeConference Paperen_US

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