Temporal Data Mining in Dynamic Feature Spaces

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 in this area. This paper presents FAE, an incremental ensemble approach to mining data subject to such concept drift. Empirical results on large data streams demonstrate promise.