Ensembles of learning machines constitute one of the main current directions in machine
learning research, and have been applied to a wide range of real problems. Despite of the …

Information fusion research has recently focused on the characteristics of the decision
profiles of ensemble members in order to optimize performance. These characteristics are …

In this work we present a large-scale comparison of 21 learning and aggregation methods
proposed in the ensemble learning, social choice theory (SCT), information fusion and …

Ensemble methods have recently garnered a great deal of attention in the machine learning
community. Techniques such as Boosting and Bagging have proven to be highly effective …

Broad classes of statistical classification algorithms have been developed and applied
successfully to a wide range of real-world domains. In general, ensuring that the particular …

The traditional motivation behind feature selection algorithms is to find the best subset of
features for a task using one particular learning algorithm. Given the recent success of …

The goal of ensemble regression is to combine several models in order to improve the
prediction accuracy in learning problems with a numerical target variable. The process of …

We propose a combined fusion-selection approach to classifier ensemble design. Each
classifier in the ensemble is replaced by a miniensemble of a pair of subclassifiers with a …

Ensemble learning constitutes one of the most fundamental and reliable strategies for
building powerful and accurate predictive models, aiming to exploit the predictions of a …

This note presents a chronological review of the literature on ensemble learning which has
accumulated over the past twenty years. The idea of ensemble learning is to employ multiple …