… machine learning as concept drift. We … concepts using an adaptive time window. The method
uses a significance test to detect concept drift and then optimizes the size of the time window…

… Time window optimisation. This section presents an approach that learns an up-to-date
classifier on drifting concept by dynamic optimisation of the time window size to gain maximum …

… machine learning as concept drift. We … concepts using an adaptive time window. The method
uses a significance test to detect concept drift and then optimizes the size of the time window…

… machine learning as concept drift. We … concepts using an adaptive time window. The method
uses a significance test to detect concept drift and then optimizes the size of the time window…

… observable and may change time to time. Drift also occurs in monitoring tasks and predictive
… The optimization criteria for change detection is to minimize the detection delay (from the …

… special conditions of concept drift when the same concept is seen after a long time. Sudden
drift is … It dynamically adjusts the drift checkpoint and window size to track the concept drift. …

… learning algorithms incremental and optimizing the balance of … , while techniques for tracking
changing prior probabilities … based on a short recent time window. The technique uses the …

… 1 is implemented by a landmark time window, as shown in Fig. … construct a training set that
continually tracked a new concept. … problems in big data by an incrementally optimized stream …

… Slow concept drift can then be tracked initially as the old … Optimization of k nearest neighbor
density estimates. Inf. … Using multiple windows to track concept drift. IDA 8(1), 29–59 (2004) …

… -to-date classifier on drifting concept by dynamic optimisation of the time window size to …
window size there are two important questions that have to be addressed in case of concept drift…