In this studywe compared the performance of regression tree ensembles using hyperspectral data. More specifically, we compared the performance of bagging, boosting and random forest to predict Sirex noctilio induced water stress in Pinus patula trees using nine spectral parameters derived from hyperspectral data. Results from the study show that the random forest ensemble achieved the best over...

This article investigates the properties of class-switching ensembles composed of neural networks and compares them to class-switching ensembles of decision trees and to standard ensemble learning methods, such as bagging and boosting. In a class-switching ensemble, each learner is constructed using a modified version of the training data. This modification consists in switching the class label...

Ensemble learning combines several individual models to obtain better generalization performance. Currently, deep architectures are showing performance compared the shallow or traditional models. Deep ensemble combine advantages of both as well such that final model has This paper reviews state-of-art and hence serves an extensive summary for researchers. The broadly categorized into bagging, b...

This paper investigates the possibility of using ensemble algorithms to improve the performance of network intrusion detection systems. We use an ensemble of three different methods, bagging, boosting and stacking, in order to improve the accuracy and reduce the false positive rate. We use four different data mining algorithms, naïve bayes, J48 (decision tree), JRip (rule induction) and iBK( ne...

Classifier combination techniques have been applied to a number of natural language processing problems. This paper explores the use of bagging and boosting as combination approaches for coreference resolution. To the best of our knowledge, this is the first effort that examines and evaluates the applicability of such techniques to coreference resolution. In particular, we (1) outline a scheme ...

Ensemble learning methods have received remarkable attention in the recent years and led to considerable advancement in the performance of the regression and classification problems. Bagging and boosting are among the most popular ensemble learning techniques proposed to reduce the prediction error of learning machines. In this study, bagging and gradient boosting algorithms are incorporated in...

While both cost-sensitive learning and online learning have been studied extensively, the effort in simultaneously dealing with these two issues is limited. Aiming at this challenge task, a novel learning framework is proposed in this paper. The key idea is based on the fusion of online ensemble algorithms and the state of the art batch mode cost-sensitive bagging/boosting algorithms. Within th...