Online churn detection on high dimensional cellular data using adaptive hierarchical trees

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dc.citation.epage2279en_US
dc.citation.spage2275en_US
dc.contributor.authorKhan, Farhanen_US
dc.contributor.authorDelibalta, İ.en_US
dc.contributor.authorKozat, Süleyman Serdaren_US
dc.coverage.spatialBudapest, Hungaryen_US
dc.date.accessioned2018-04-12T11:49:41Z
dc.date.available2018-04-12T11:49:41Z
dc.date.issued2016en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionDate of Conference: 29 August-2 September 2016en_US
dc.descriptionConference Name: 24th European Signal Processing Conference, EUSIPCO 2016en_US
dc.description.abstractWe study online sequential logistic regression for churn detection in cellular networks when the feature vectors lie in a high dimensional space on a time varying manifold. We escape the curse of dimensionality by tracking the subspace of the underlying manifold using a hierarchical tree structure. We use the projections of the original high dimensional feature space onto the underlying manifold as the modified feature vectors. By using the proposed algorithm, we provide significant classification performance with significantly reduced computational complexity as well as memory requirement. We reduce the computational complexity to the order of the depth of the tree and the memory requirement to only linear in the intrinsic dimension of the manifold. We provide several results with real life cellular network data for churn detection.en_US
dc.identifier.doi10.1109/EUSIPCO.2016.7760654en_US
dc.identifier.urihttp://hdl.handle.net/11693/37740
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttps://doi.org/10.1109/EUSIPCO.2016.7760654en_US
dc.source.titleProceedings of the 24th European Signal Processing Conference, EUSIPCO 2016en_US
dc.subjectBig dataen_US
dc.subjectChurnen_US
dc.subjectClassification on high dimensional manifoldsen_US
dc.subjectOnline learningen_US
dc.subjectTree based methoden_US
dc.titleOnline churn detection on high dimensional cellular data using adaptive hierarchical treesen_US
dc.typeConference Paperen_US
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