Abstract The remarkable advances in ensemble machine learning methods have led to a significant analysis large data, such as random forest algorithms. However, the algorithms only use current features during process of learning, which caused initial upper accuracy’s limit no matter how well are. Moreover, low classification accuracy happened especially when one type observation’s proportion is ...

We study weighted ensemble, an interacting particle method for sampling distributions of Markov chains that has been used in computational chemistry since the 1990s. Many important applications ensemble require computation long time averages. establish consistency this setting by proving ergodic theorem As part proof, we derive explicit variance formulas could be useful optimizing method.

Recently, ensemble learning methods have been widely used to improve classification performance in machine learning. In this paper, we present a novel ensemble learning method: argumentation based multi-agent joint learning (AMAJL), which integrates ideas from multi-agent argumentation, ensemble learning, and association rule mining. In AMAJL, argumentation technology is introduced as an ensemb...

Ensemble selection has recently appeared as a popular ensemble learning method, not only because its implementation is fairly straightforward, but also due to its excellent predictive performance on practical problems. The method has been highlighted in winning solutions of many data mining competitions, such as the Netflix competition, the KDD Cup 2009 and 2010, the UCSD FICO contest 2010, and...

The Ensemble of Classifiers (EoC) has been shown to be effective in improving the performance of single classifiers by combining their outputs, and one of the most important properties involved in the selection of the best EoC from a pool of classifiers is considered to be classifier diversity. In general, classifier diversity does not occur randomly, but is generated systematically by various ...

In many practical classification problems, mislabeled data instances (i.e., class noise) exist in the acquired (training) data and often have a detrimental effect on the classification performance. Identifying such noisy instances and removing them from training data can significantly improve the trained classifiers. One such effective noise detector is the so-called ensemble filter, which pred...

Sequential aggregation is an ensemble forecasting approach that weights each ensemble member based on past observations and past forecasts. This approach has several limitations: the weights are computed only at the locations and for the variables that are observed, and the observational errors are typically not accounted for. This paper introduces a way to address these limitations by coupling...

to perform any economic management of a petroleum reservoir in real time, a predictable and/or updateable model of reservoir along with uncertainty estimation ability is required. one relatively recent method is a sequential monte carlo implementation of the kalman filter: the ensemble kalman filter (enkf). the enkf not only estimate uncertain parameters but also provide a recursive estimate of...

This report extends a recent method that calculates an ensemble of solutions of the Navier-Stokes equations efficiently to higher Reynolds number flows. To do so herein we develop and analyze two ensemble eddy viscosity models that do not obviate the good algorithmic properties of the ensemble method. The combined approach of ensemble time stepping and ensemble eddy viscosity modelling has othe...