… based classifier involves two critical decisions: (1) Selection of base learners, which are …
powers of base-learners. In order to train and test our super-learner ensemble, I propose to use …

… various experiments using 14 experimental datasets that consist of low-dimensional
balanced, high-dimensional balanced, and high-dimensional imbalanced datasets. Finally, we …

… In the first stage, we introduce SMOTE-CVCF to integrate filter technique for resampling the
input dataset. To the best of our knowledge, as an excellent oversampling technique, SMOTE …

… using a moving average filter, where each sample of … ensemble members using an online
learner [66, 73]; and/or (3) add new members to build an ensemble with each incoming dataset…

… how we constructed our datasets, the ensemble techniques and base learners we use, and
… Prior to extracting features, text was filtered and cleaned by removing symbols, punctuation …

… the “boosting by filtering” ensemble learning framework. In the “boosting by filtering” framework
ensembles are … methods—this motivates our inclusion of results on familiar ML data sets. …

… be challenging, especially when learning 24 from high-dimensional and imbalanced datasets
[7],[… datasets [10]. Therefore, researchers have frequently resorted to new and 28 improved …

… of online ensemble learning methods to noise present in data streams. The 12 datasets …
streams are based either on costly filters or using specific learning algorithms. Our aim was …

… becomes complex when learning from imbalanced data sets, very few … We propose a
modification in ensemble based bagging … We found that the modified bagged ensemble makes an …

… the most well-known ensemble learning methods due to their … However, new ensemble
learning techniques on the feature … a filter is its variability of results over different data sets. That …