11 search results - page 1 / 3 » DDD: A New Ensemble Approach for Dealing with Concept Drift |
TKDE
2012
11 years 7 months ago
2012
—Online learning algorithms often have to operate in the presence of concept drifts. A recent study revealed that different diversity levels in an ensemble of learning machines a...
ICML
2005
IEEE
14 years 5 months ago
2005
IEEE
We consider online learning where the target concept can change over time. Previous work on expert prediction algorithms has bounded the worst-case performance on any subsequence ...
CIS
2004
Springer
13 years 10 months ago
2004
Springer
Concept drifting in data streams often occurs unpredictably at any time. Currently many classification mining algorithms deal with this problem by using an incremental learning ap...
SDM
2007
SIAM
13 years 6 months ago
2007
SIAM
In recent years, there have been some interesting studies on predictive modeling in data streams. However, most such studies assume relatively balanced and stable data streams but...
MCS
2009
Springer
13 years 9 months ago
2009
Springer
We have recently introduced an incremental learning algorithm, Learn++ .NSE, for Non-Stationary Environments, where the data distribution changes over time due to concept drift. Le...