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We present the ideas and methodologies that we used to address the KDD Cup 2009 challenge on rank-ordering the probability of churn, appetency and up-selling of wireless customers. We choose stochastic gradient boosting tree (TreeNet R) as our main classifier to handle this large unbalanced dataset. In order to further improve the robustness and accuracy of… (More)

— The traffic behavior of University of Louisville network with the interconnected backbone routers and the number of Virtual Local Area Network (VLAN) subnets is investigated using the Random Matrix Theory (RMT) approach. We employ the system of equal interval time series of traffic counts at all router to router and router to subnet connections as a… (More)

— To observe the evolution of network traffic correlations we analyze the eigenvalue spectra and eigenvectors statistics of delayed correlation matrices of network traffic counts time series. Delayed correlation matrix D (τ) is composed of the correlations between one variable in the multivariable time series and another at a time delay τ. We determined,… (More)

The traffic behavior of the University of Louisville network with the interconnected backbone routers and the number of Virtual Local Area Network (VLAN) subnets is investigated using the Random Matrix Theory (RMT) approach. We employ the system of equal interval time series of traffic counts at all router to router and router to subnet connections as a… (More)

Feature extraction involves simplifying the amount of resources required to describe a large set of data accurately. In this paper, we propose to broaden the feature extraction algorithms with Random Matrix Theory methodology. Testing the cross-correlation matrix of variables against the null hypothesis of random correlations, we can derive characteristic… (More)

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