… In this research, the classification task is used to evaluate student’s performance and as …
the use of several models, such as ensembles or committees, since they produce robust and fast …

… is to compile an alternative classification whenever the base classifiers demonstrate varied
… A combinational incremental ensemble of classifiers as a technique for predicting students’ …

… that SVM classifiers outperform the traditional pattern classifiers (… SVM classification accuracy
in the gender classification system … The result is a weighted ensemble classifier that could …

… classifier is needed to perform classification, while the KNNENS supports simultaneous
classification and detection using the … sub-model for all classes, which ensures the KNNENS to …

… we can use ensemble strategies for existing studies to improve the data … classification
performance. In this paper, we propose a new dynamic incremental ensemble fuzzy classification …

… , this work combines incremental ML classifiers using the ensemble method. The ensemble
method is an … ensemble is a set of classifiers of different types built upon the same data [36]. …

… for text classifier training, we can apply incremental learning to resolve the text classification
… ’s distribution to form an ensemble classifier as different Student models. We expect to see …

… a chunk-based incremental ensemble algorithm called Dynamic Updated Ensemble (DUE) …
of this paper is to make the proposed classifier cope with the problem of switching classes. …

… ensemble approaches dealing with classification tasks in non-stationary data streams. For a
reference that covers a wide range of ensemble … new incremental classifier to the ensemble …

… classification algorithms to predict students’ performance. Panda (2019) introduced a hybrid
… A combinational incremental ensemble of classifiers as a technique for predicting students’ …