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Temporal Data Mining In A Dynamic Feature Space, Brent K. Wenerstrom
Temporal Data Mining In A Dynamic Feature Space, Brent K. Wenerstrom
Theses and Dissertations
Many interesting real-world applications for temporal data mining are hindered by concept drift. One particular form of concept drift is characterized by changes to the underlying feature space. Seemingly little has been done to address this issue. This thesis presents FAE, an incremental ensemble approach to mining data subject to concept drift. FAE achieves better accuracies over four large datasets when compared with a similar incremental learning algorithm.
Detecting Potential Insider Threats Through Email Datamining, James S. Okolica
Detecting Potential Insider Threats Through Email Datamining, James S. Okolica
Theses and Dissertations
No abstract provided.