During the last decades, in the area of machine learning and data mining, the development
of ensemble methods has gained a significant attention from the scientific community …

Among a number of ensemble learning techniques, boosting and bagging are the most
popular sampling-based ensemble approaches for classification problems. Boosting is …

Ensemble learning is a prolific field in Machine Learning since it is based on the assumption
that combining the output of multiple models is better than using a single model, and it …

Ensemble learning is a powerful machine learning paradigm which has exhibited apparent
advantages in many applications. An ensemble in the context of machine learning can be …

Ensemble learning is fast becoming a popular choice for machine learning models in the
data science world. Ensemble methods combine the output of machine learning models in …

Ensemble methods are learning algorithms that construct a set of classifiers and then
classify new data points by taking a (weighted) vote of their predictions. The original …

Feature selection has become an indispensable preprocessing step in an expert system.
Improving the feature selection performance could guide such a system to make better …

Boosting is recognized as one of the most successful techniques for generating classifier
ensembles. Typically, the classifier outputs are combined by the weighted majority vote. The …

Ensemble selection deals with the reduction of an ensemble of predictive models in order to
improve its efficiency and predictive performance. The last 10 years a large number of very …

Random forests are one of the best performing methods for constructing ensembles. They
derive their strength from two aspects: using random subsamples of the training data (as in …