The idea of ensemble methodology is to build a predictive model by integrating multiple models. It is well-known that ensemble methods can be used for improving prediction performance. Researchers from various disciplines such as statistics, machine learning, pattern recognition, and data mining have considered the use of ensemble methodology. Stacking is a general ensemble method in which a nu...

Monte Carlo direct search methods, such as genetic algorithms, simulated annealing etc., are often used to explore a finite dimensional parameter space. They require the solving of the forward problem many times, that is, making predictions of observables from an earth model. The resulting ensemble of earth models represents all ‘information’ collected in the search process. Search techniques h...

We propose a new method for training an ensemble of neural networks. A population of networks is created and maintained such that more probable networks replicate and less probable networks vanish. Each individual network is updated using random weight changes. This produces a diversity among the networks which is important for the ensemble prediction using the population. The method is compare...

Evoked potentials (EP) are electrophysiological signals generated by the central nervous system in response to stimulation. Subspace methods in EP analysis commonly relate to the application of the singular value decomposition (SVD) to reveal the principal features of the signals. In single-channel EP analysis, the measurement matrix is constructed based on an ensemble of singletrial EPs, obtai...

Recent advances in Machine Learning have brought to attention new theories of learning as well as new approaches. Among these, the Ensemble method has received wide attention and has been shown to be a promising method for classification problems. Simply speaking, the ensemble method is a learning algorithm that constructs a set of “weak” classifiers and then combines their predictions to produ...

Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble techniques are known to be very useful in improving the generalization ability of a classifier. The random subspace ensemble technique is a simple but effective method of constructing ensemble classifiers, in which some features are randomly d...

Ensemble methods for supervised machine learning have become popular due to their ability to accurately predict class labels with groups of simple, lightweight “base learners.” While ensembles offer computationally efficient models that have good predictive capability they tend to be large and offer little insight into the patterns or structure in a dataset. We consider an ensemble technique th...

Ensemble methods improve accuracy by combining the predictions of a set of different hypotheses. However, there is an important shortcoming associated with ensemble methods. Huge amounts of memory are required to store a set of multiple hypotheses. In this work we devise an ensemble method that partially solves this drawbacks. The key point is that components share their common parts. For this ...

When there is no clear separation between microand macro-scales, ergodicity cannot be invoked to transform ensemble into volume averages. In such cases it is necessary to use ensemble averaging directly. The calculation of such averages, however, converges slowly and therefore requires a large number of realizations of the system. This paper describes a much more efficient method based on the u...

In this paper we make an extensive study of different methods for building ensembles of classifiers. We examine variants of ensemble methods that are based on perturbing features. We illustrate the power of using these variants by applying them to a number of different problems. We find that the best performing ensemble is obtained by combining an approach based on random subspace with a cluste...