In the context of variable selection, ensemble learning has gained increasing interest due to its great potential to improve selection accuracy and to reduce false discovery rate. A novel ordering-based selective ensemble learning strategy is designed in this paper to obtain smaller but more accurate ensembles. In particular, a greedy sorting strategy is proposed to rearrange the order by which...

Feature selection for ensembles has shown to be an effective strategy for ensemble creation. In this paper we present an ensemble feature selection approach based on a hierarchical multi-objective genetic algorithm. The first level performs feature selection in order to generate a set of good classifiers while the second one combines them to provide a set of powerful ensembles. The proposed met...

Numerical ensemble forecasting is a powerful tool that drives many risk analysis efforts and decision making tasks. These ensembles are composed of individual simulations that each uniquely model a possible outcome for a common event of interest: e.g. the direction and force of a hurricane, or the path of travel and mortality rate of a pandemic. In this context, numerical ensemble data represen...

classification ensemble, which uses the weighed polling of outputs, is the art of combining a set of basic classifiers for generating high-performance, robust and more stable results. this study aims to improve the results of identifying the persian handwritten letters using error correcting output coding (ecoc) ensemble method. furthermore, the feature selection is used to reduce the costs of ...

The objective of this paper is to define an effective strategy for building an ensemble of Genetic Programming (GP) models. Ensemble methods are widely used in machine learning due to their features: they average out biases, they reduce the variance and they usually generalize better than single models. Despite these advantages, building ensemble of GP models is not a well-developed topic in th...

Information fusion research has recently focused on the characteristics of the decision profiles of ensemble members in order to optimize performance. These characteristics are particularly important in the selection of ensemble members. However, even though the control of overfitting is a challenge in machine learning problems, much less work has been devoted to the control of overfitting in s...

This paper studies the ensemble selection problem for unsupervised learning. Given a large library of different clustering solutions, our goal is to select a subset of solutions to form a smaller but better performing cluster ensemble than using all available solutions. We design our ensemble selection methods based on quality and diversity, the two factors that have been shown to influence clu...

Customer churn prediction is one of the most important elements of any Customer Relationship Management (CRM) strategy. In this study, a number of strategies are investigated to increase the lift of ensemble classification models. In order to increase lift performance, two elements of a number of well-known ensemble strategies are altered: (i) the potential of using probability estimation trees...