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.
College and Department
Physical and Mathematical Sciences; Computer Science
BYU ScholarsArchive Citation
Wenerstrom, Brent K., "Temporal Data Mining in a Dynamic Feature Space" (2006). All Theses and Dissertations. 761.
incremental learning, data mining, spam, ensemble, machine learning