Temporal Data Mining in a Dynamic Feature Space

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.

Name thesis:degree_name

MS

Grantor dc:publisher

Brigham Young University - Provo

Author dc:creator

• Wenerstrom, Brent K.

• incremental learning
• data mining
• spam
• ensemble
• machine learning
• Computer Sciences

Language dc:language

English

Repository record dc:identifier

https://scholarsarchive.byu.edu/etd/761

OAI identifier oai:identifier

oai:scholarsarchive.byu.edu:etd-1760