Online churn detection on high dimensional cellular data using adaptive hierarchical trees
dc.citation.epage | 2279 | en_US |
dc.citation.spage | 2275 | en_US |
dc.contributor.author | Khan, Farhan | en_US |
dc.contributor.author | Delibalta, İ. | en_US |
dc.contributor.author | Kozat, Süleyman Serdar | en_US |
dc.coverage.spatial | Budapest, Hungary | en_US |
dc.date.accessioned | 2018-04-12T11:49:41Z | |
dc.date.available | 2018-04-12T11:49:41Z | |
dc.date.issued | 2016 | en_US |
dc.department | Department of Electrical and Electronics Engineering | en_US |
dc.description | Date of Conference: 29 August-2 September 2016 | en_US |
dc.description | Conference Name: 24th European Signal Processing Conference, EUSIPCO 2016 | en_US |
dc.description.abstract | We 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.description.provenance | Made available in DSpace on 2018-04-12T11:49:41Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 179475 bytes, checksum: ea0bedeb05ac9ccfb983c327e155f0c2 (MD5) Previous issue date: 2016 | en |
dc.identifier.doi | 10.1109/EUSIPCO.2016.7760654 | en_US |
dc.identifier.uri | http://hdl.handle.net/11693/37740 | |
dc.language.iso | English | en_US |
dc.publisher | IEEE | en_US |
dc.relation.isversionof | https://doi.org/10.1109/EUSIPCO.2016.7760654 | en_US |
dc.source.title | Proceedings of the 24th European Signal Processing Conference, EUSIPCO 2016 | en_US |
dc.subject | Big data | en_US |
dc.subject | Churn | en_US |
dc.subject | Classification on high dimensional manifolds | en_US |
dc.subject | Online learning | en_US |
dc.subject | Tree based method | en_US |
dc.title | Online churn detection on high dimensional cellular data using adaptive hierarchical trees | en_US |
dc.type | Conference Paper | en_US |
Files
Original bundle
1 - 1 of 1
Loading...
- Name:
- Online churn detection on high dimensional cellular data using adaptive hierarchical trees.pdf
- Size:
- 339.19 KB
- Format:
- Adobe Portable Document Format
- Description:
- Full printable version