We empirically evaluate several state-of-the-art methods for constructing ensembles of
heterogeneous classifiers with stacking and show that they perform (at best) comparably to …

Over the last two decades, the machine learning and related communities have conducted
numerous studies to improve the performance of a single classifier by combining several …

Stacking is a general ensemble method in which a number of base classifiers are combined
using one meta-classifier which learns their outputs. Such an approach provides certain …

This paper deals with the combination of classification models that have been derived from
running different (heterogeneous) learning algorithms on the same data set. We focus on the …

Our hypothesis is that building ensembles of small sets of strong classifiers constructed with
different learning algorithms is, on average, the best approach to classification for real-world …

Ensemble methods that train multiple learners and then combine them for use, with Boosting
and Bagging as representatives, are a kind of state-of-theart learning approach. It is well …

There are two main paradigms in combining different classification algorithms: Classifier
Selection and Classifier Fusion. The first one selects a single model for classifying a new …

We propose a probabilistic framework for classifier combination, which gives rigorous
optimality conditions (minimum classification error) for four combination methods: majority …

An ensemble consists of a set of individually trained classifiers (such as neural networks or
decision trees) whose predictions are combined when classifying novel instances. Previous …

Using an ensemble of classifiers, instead of a single classifier, can lead to improved
generalization. The gains obtained by combining, however, are often affected more by the …