Browsing by Subject "Churn"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemOpen Access
    Online churn detection on high dimensional cellular data using adaptive hierarchical trees
    (IEEE, 2016) Khan, Farhan; Delibalta, İ.; Kozat, Süleyman Serdar
    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.
  • Thumbnail Image
    ItemOpen Access
    Sequential churn prediction and analysis of cellular network users-a multi-class, multi-label perspective
    (IEEE, 2017) Khan, Farhan; Kozat, Süleyman Serdar
    We 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.