Browsing by Author "Khan, Farhan"
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Item Open Access Boosted adaptive filters(Elsevier, 2018) Kari, Dariush; Mirza, Ali H.; Khan, Farhan; Özkan, H.; Kozat, Süleyman SerdarWe introduce the boosting notion of machine learning to the adaptive signal processing literature. In our framework, we have several adaptive filtering algorithms, i.e., the weak learners, that run in parallel on a common task such as equalization, classification, regression or filtering. We specifically provide theoretical bounds for the performance improvement of our proposed algorithms over the conventional adaptive filtering methods under some widely used statistical assumptions. We demonstrate an intrinsic relationship, in terms of boosting, between the adaptive mixture-of-experts and data reuse algorithms. Additionally, we introduce a boosting algorithm based on random updates that is significantly faster than the conventional boosting methods and other variants of our proposed algorithms while achieving an enhanced performance gain. Hence, the random updates method is specifically applicable to the fast and high dimensional streaming data. Specifically, we investigate Recursive Least Square-based and Least Mean Square-based linear and piecewise-linear regression algorithms in a mixture-of-experts setting and provide several variants of these well-known adaptation methods. Furthermore, we provide theoretical bounds for the computational complexity of our proposed algorithms. We demonstrate substantial performance gains in terms of mean squared error over the base learners through an extensive set of benchmark real data sets and simulated examples.Item Open Access Online churn detection on high dimensional cellular data using adaptive hierarchical trees(IEEE, 2016) Khan, Farhan; Delibalta, İ.; Kozat, Süleyman SerdarWe 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.Item Open Access Online nonlinear modeling for big data applications(2017-12) Khan, FarhanWe investigate online nonlinear learning for several real life, adaptive signal processing and machine learning applications involving big data, and introduce algorithms that are both e cient and e ective. We present novel solutions for learning from the data that is generated at high speed and/or have big dimensions in a non-stationary environment, and needs to be processed on the y. We speci cally focus on investigating the problems arising from adverse real life conditions in a big data perspective. We propose online algorithms that are robust against the non-stationarities and corruptions in the data. We emphasize that our proposed algorithms are universally applicable to several real life applications regardless of the complexities involving high dimensionality, time varying statistics, data structures and abrupt changes. To this end, we introduce a highly robust hierarchical trees algorithm for online nonlinear learning in a high dimensional setting where the data lies on a time varying manifold. We escape the curse of dimensionality by tracking the subspace of the underlying manifold and use the projections of the original high dimensional regressor space onto the underlying manifold as the modi ed regressor vectors for modeling of the nonlinear system. By using the proposed algorithm, 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 demonstrate the signi cant performance gains in terms of mean square error over the other state of the art techniques through simulated as well as real data. We then consider real life applications of online nonlinear learning modeling, such as network intrusions detection, customers' churn analysis and channel estimation for underwater acoustic communication. We propose sequential and online learning methods that achieve signi cant performance in terms of detection accuracy, compared to the state-of-the-art techniques. We speci cally introduce structured and deep learning methods to develop robust learning algorithms. Furthermore, we improve the performance of our proposed online nonlinear learning models by introducing mixture-of-experts methods and the concept of boosting. The proposed algorithms achieve signi cant performance gain over the state-ofthe- art methods with signi cantly reduced computational complexity and storage requirements in real life conditions.Item Open Access Sequential churn prediction and analysis of cellular network users-a multi-class, multi-label perspective(IEEE, 2017) Khan, Farhan; Kozat, Süleyman SerdarWe 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.