… The stream data classification model without concept drift adaptation is not preferable to the
… ensemble classification technique, named as Handling Imbalanced Data with Concept Drift (…

… Ensemble classifiers consist of a set of classifiers whose individual decisions are combined
… ensemble classification in streaming data is their capacity to cope with recurring concept drift…

… We plot a ROC curve that shows the performance of an ensemble classification model. In
FIGURE 3b ROC curve that passes through the upper left corner shows 100% sensitivity and …

… Adaptive depth units with different layers are looked upon as base classifiers for the ensemble,
and concept drift data are processed by dynamically adjusting the weights of each base …

… ensemble models represent a popular group of these methods used in classification of drifting
… the heterogeneous adaptive ensemble model for the data streams classification, which …

… as candidate solutions, and ensemble classifiers have demonstrated the ability to handle
different types of drifting concepts in nonstationary environments. Hassan [6] proposed a …

… This also proves that an update mechanism proposed in this article can provide more effective
base classifiers for a data stream with concept drift. Due to the limited resources, such as …

… accuracies in scenarios with concept drifts. Despite its specific application to ensembles in
this work… to other contexts eg, optimization of classifiers’ or concept drift detectors’ parameters. …

… in concept drift using ensemble methods and also provides a comprehensive introduction
to ensemble methods, data stream classification models, types of concept drift and drift …

… stream classification from three different perspectives. We begin by reviewing the methods
for concept drift adaptation in the literature (1), then we present the ensemble approaches …