-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 …

A 6-month drought severity index (DSI6) is applied to each of the 11-member perturbed-physics ensemble (HadRM3-PPE … droughts. Projections of drought characteristics are expressed in the forms of ensemble-mean change relative to the 1980s and … particular relevance to water management, results from this ensemble have a strong influence on dry season water availability …

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 …

management. Experimental results have shown that ensemble of classifiers are often more accurate and robust to the effects of … circumstances. Therefore, more research is needed to evaluate the performance of ensemble algorithms in software defect prediction …. The goal of this paper is to assess the quality of ensemble methods in software defect prediction with the analytic …

correlation exists as an ensemble for the 6-year time period in the evolution of the system. We verify the structure of group …

improve the outcomes, making use of techniques that combine classifiers named ensemble methods. Therefore they proposed two … Support Vector Regression. Their ensemble approach outperformed the classical approaches as simple classifiers and ensemble …

In this paper, a hierarchical ensemble classification approach that utilizes a Directed Acyclic Graph (DAG) structure …

-rough feature selection model with an adaptive neural net ensemble. Fuzzy-rough method deals with uncertainty and impreciseness of … of the hybrid-FRGSNN model is evaluated by the proposed neural net ensemble learning algorithm. Again to prove the … learning capability of ensemble algorithm, performance of the component classifiers pairing with FR, GSNN and FRGSNN are …