… our classifier, the Stacked multi-class ISVM is tested using a … classifier is a multi-class ISVM,
we compare our method to four pertinent batch ensemble learning models and classification …

… rejuvenating an individual classifier if it significantly impacts the classifier ensemble, ie, if
its … four factors tested in six pairs using sudden, gradual, and incremental drift using three …

… methodology and combined the incremental version of Naïve … The main objective of this
research is to classify students who … and other ensemble methods have classified the attributes …

… , which is based on the Damped Incremental Statistics algorithm for feature selection and …
In the classification step, an ensemble classifier combined three different algorithms, C4.5, …

… incremental Hoeffding trees are not, in fact, unstable learners, unlike their batch counterparts,
and how this fact significantly influences ensemble … other ensemble classifiers as shown in […

… Our result showed an increase in performance of classifiers with adaptive incremental …
Random forest [16] is a popular ensemble learning approach for classification and regression task…

… of weak classifiers and reducing the generation of weak classifiers. Moreover, this … ensemble
learning to provide a kind of incremental learning approach rather than discuss ensemble …

… We show that SSE-PBS improves classification performance and handles … classifiers
incrementally instead of using batch static learners. Since the components are incrementally …

… , the basic concepts of Support Vector Classifier (SVM) are … final answer for the boost increment
within the perform space, … on the same dataset using the ensemble voting classifier [14]. …

… Specifically, the AECF follows an online and incremental learning approach and depends …
of drifts while using an ensemble of classifiers to constantly reset low-performance classifiers. …