… [12] demonstrated that the novel ensemble classifier (RFAODE) for intrusion detection …
For this purpose, we evaluated AIDM using seven different classification techniques able to …

… ensemble classifier to handle recurring concepts; that is, which previously learned classification
models should be preserved for future use… chunk based incremental ensemble algorithm …

… detection, where the ensemble is updated through an incremental stream-oriented learning
… induce the base classifiers of our ensembles (ie, to populate the list learners of algorithms in …

… OSELM and new classes of data to adapt the old ELM to a new classifier gradually. For data
… component comprises incremental and active learning, while an ensemble classifier has …

… incremental attribute reordering based on a binary classifier … ensemble classifier selection
and the process to combine the results to boost the performance of the ensemble classification …

… removed ensemble components and the ensemble itself, we create an ensemble of classifiers
of … The chosen datasets contain all types of concept drift (incremental, gradual, abrupt, and …

… The naive approach of finetuning, so fruitfully applied to domain transfer problems, suffers
from the lack of data from previous tasks and the resulting classifier is unable to classify data …

… classification at each iteration. Then, a newly trained weak classifier is added to the ensemble
classifier… [17] to improve the effect of classification on a few categories. Most of research of …

… are powerful in improving accuracy of incremental … classifiers and ensemble methods. In
the category of explicit drift detection techniques, concept drift is signalled through classification …

… classification plays a key role in big data analytics due to its enormous growth. Most of the
existing classification methods used ensemble … maintains the ensemble of the classifier with …