… for incrementally constructing a batch-mode ensemble using … the impact of diversity in
ensemble classifiers and show the … for the binary classification problem. We select a binary …

… ensemble classifier smart pools of data with ensembles for class imbalance adaptive learning
(SPECIAL) to classify … incremental ensemble capable of handling recurring concept drifts …

… and must update its model incrementally as each example is … accuracy of classifiers that
make predictions using the current … of H stores an ensemble of classifiers using different lambda …

… in the ensemble learning literature, each ensemble member … classifies a sample to one of
classes or is far from decision … second dominant classes examined by the classifier’s outputs. …

… Ensemble Classification (OEC) algorithm to integrate the predictions obtained by different
base online classification … severs for generating synthetic data of incremental drift. In addition, …

… from the construction of ensembles of classifiers and the step-… Thus, our proposal is an
incremental method of the first type … over classes with few patterns is almost disregarded if we use …

… [9] used a training algorithm which is the combination of unsupervised incremental classification
and supervised classification, achieving overall 97.57% accuracy. In Zander et al. [10], …

… combination function for the base classifiers. In classification, most combination methods …
from base classifiers. The most common approach is to take a majority vote, where every …

… In this paper, an open set incremental learning method is … classes continually. The proposed
method, open set model with incremental learning (OSmIL), is an ensemble classifier so it is …

… to improved adaptation to various types of concept drift and … We use a set of classifiers to
decide which instances should … A, we can formulate an incremental update of hypothesis h in i-…