… exemplars from old classes. Hence, during the b-th incremental stage, we can only access
data from Db for training. In CIL, we aim to build a unified classifier for all seen classes Yb = …

… ensemble models, named incremental weighted ensemble (IWE) and incremental weighted
ensemble for multi-classification (IWE… At each time step, all base classifiers are incrementally …

… , student performance is classified into … an incremental ensemble consisting of three classifiers
(naive Bayes, K-star, and SVM) and used a voting scheme to predict the career of students…

… Ensemble (ROSE), a novel online ensemble classifier capable … the online incremental learning
with the dynamic ensemble … to classify new instances ROSE combines its base classifiers …

… Our classifier ensemble promotes regularizing network parameters or activations when
moving to … a set of K side classifiers, each of which learns to correctly classify the same \(C^{t}\) …

… a novel incremental layered classifier selection approach is … the proposed ensemble classifier
we calculated classification … The classification accuracy is calculated using equation (11). …

… to classify were low, medium and high and the classifier was … develop an algorithm with
incremental learning to mine data … by a specific classification technique is the use of ensemble …

… At present, all of Weka's classifiers and filters are available in the … using classifier performance
evaluation. This function evaluates the performance of incrementally trained classifiers. …

… classifiers some of which are ensemble classifiers. These … : binary classification cases
where students are classified as … ENSEMBLE LEARNING Boosting is an incremental process …

… Furthermore, we update the ensemble with new classifiers … This mechanism implements
both incremental learning of new … KUE, a new ensemble classification algorithm for drifting data …