In this paper, we investigate the method of stacked generalization in combining models derived from diierent subsets of a training dataset by a single learning algorithm, as well as diierent algorithms. The simplest way to combine predictions from competing models is majority vote, and the eeect of the sampling regime used to generate training subsets has already been studied in this context|wh...

Classiier committee learning approaches have demonstrated great success in increasing the prediction accuracy of classiier learning , which is a key technique for datamining. These approaches generate several classiiers to form a committee by repeated application of a single base learning algorithm. The committee members vote to decide the nal classiication. It has been shown that Boosting and ...

The problem of training classifiers only with target data arises in many applications where non-target data are too costly, difficult to obtain, or not available at all. Several one-class classification methods have been presented to solve this problem, but most of the methods are highly sensitive to the presence of outliers in the target class. Ensemble methods have therefore been proposed as ...

This paper presents the application of the bagging technique for non-linear regression models to obtain more accurate and robust calibration of spectroscopy. Bagging refers to the combination of multiple models obtained by bootstrap re-sampling with replacement into an ensemble model to reduce prediction errors. It is well suited to “non-robust” models, such as the non-linear calibration method...

The study reported was devoted to investigate to what extent bagging approach could lead to the improvement of the accuracy machine learning regression models. Four algorithms implemented in the KEEL tool, including two evolutionary fuzzy systems, decision trees for regression, and neural network, were used in the experiments. The results showed that some bagging ensembles ensured higher predic...

Theoretical and experimental analyses of bagging indicate that it is primarily a variance reduction technique. This suggests that bagging should be applied to learning algorithms tuned to minimize bias, even at the cost of some increase in variance. We test this idea with Support Vector Machines (SVMs) by employing out-of-bag estimates of bias and variance to tune the SVMs. Experiments indicate...

The ecological niche is the set of environments in which a population of a species can persist without introduction of individuals from other locations. A good mathematical or computational representation of the niche is a prerequisite to addressing many questions in ecology, biogeography, evolutionary biology and conservation. A particularly challenging question for ecological niche modelling ...

Ensemble learning has gained success in machine with major advantages over other methods. Bagging is a prominent ensemble method that creates subgroups of data, known as bags, are trained by individual methods such decision trees. Random forest example bagging additional features the process. Evolutionary algorithms have been for optimisation problems and also used learning. gradient-free work ...