Abstract
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.
Degree
MS
College and Department
Physical and Mathematical Sciences; Computer Science
Rights
http://lib.byu.edu/about/copyright/
BYU ScholarsArchive Citation
Wenerstrom, Brent K., "Temporal Data Mining in a Dynamic Feature Space" (2006). Theses and Dissertations. 761.
https://scholarsarchive.byu.edu/etd/761
Date Submitted
2006-05-22
Document Type
Thesis
Handle
http://hdl.lib.byu.edu/1877/etd1317
Keywords
incremental learning, data mining, spam, ensemble, machine learning
Language
English