… ensemble classifiers. Drift-detection-based incremental ensemble updates the weight on
ensemble … drift detection-based adaptive ensemble classifier for classification to minimize false-…

… ensemble classifier, a combination of classification algorithms. These algorithms are used
in machine learning to perform effective classifications. Different … Incremental Particle Swarm …

… Incremental learning with ensemble … classes, there are other data difficulty factors like noisy
minority instances and overlapping classes which deteriorates the performance of classifiers, …

… Concept drift detection methods generally use a base classifier to classify the incoming data
instances. The … It yields good performance in case of incremental learning in ensembles. …

… on most of classes. A hybrid ensemble of classifiers, which combines the classifiers using the
voting … an initial set of rules for the class is generated using incremental reduced error. This …

… remaining component classifiers Cj ∈ ε in the ensemble are incrementally trained with …
ensemble classifiers algorithm A-AUE2 is proposed in this paper to effectively tackle four types of …

… Ensemble size: An ensemble size of 40 classifiers was applied … We used 40 classifiers in all
ensembles used in this work. … RCARF, an ensemble tree-based online classifier that handles …

… ) and a Decision Tree classifier were combined within a stacked generalization, as a classifier
ensemble, in which individual classifier decisions are combined to classify new instances. …

… , an incremental algorithm for MIL based on classifier ensembles is … ensemble learning
approaches according to the number of classifiers used to classify the new data; as incremental …

… of classifier ensembles are another incremental adaptability strategy that incorporates several
learners … combined into a final ensemble hypothesis, using a majority voting algorithm. …