-bagging significantly outperforms state-of-the-art ensemble pruning methods such as Boosting-based pruning and Trimmed bagging. …Ensemble pruning deals with the selection of base learners prior to combination in order to improve prediction accuracy … and efficiency. In the ensemble literature, it has been pointed out that in order for an ensemble classifier to achieve …

We propose a new classification ensemble method named Canonical Forest. The new method uses canonical linear … discriminant analysis (CLDA) and bootstrapping to obtain accurate and diverse classifiers that constitute an ensemble. We note CLDA … original space. To further facilitate the diversity of the classifiers in an ensemble, CLDA is applied only on a partial …

boosting with the LDA and logistic regression and study their relative efficiencies in reducing the error rate based on the … 60% for the SVM and 50% to 80% for boosting when compared to the LDA. However, a smooth variant of the SVM is shown to be …

This letter provides a simple extension of boosting methods for binary data where the probability of mislabeling …