Nov 12, 2015 · These methods process the margins with a bipolar sigmoid function, as the most important information is contained in margins of low magnitude.

In this paper we propose a new group of methods, which use the margins of individual classifiers from the ensemble. These methods process the margins with a ...

The main goal of this paper is to investigate the relationship between two theories widely applied to explain the success of classifiers fusion: diversity ...

We focus on the relationship between ensemble diversity and ensemble margin, two fundamental theories in ensemble learning. Applying the RF classifier, we ...

... margin based measures can be useful for the evaluation and selection of ensembles of classifiers with majority voting. The main goal of this paper is to ...

Margin-based Diversity Measures for Ensemble Classifiers · List of references · Publications that cite this publication.

Jan 31, 2018 · Ensemble pruning is a technique used to improve ensemble performance and reduce the ensemble size by selecting an optimal or sub-optimal subset as the final ...

This work focuses on exploiting the margin theory to design better ensemble classifiers. We show that low margin instances have a major influence in building ...

In the non-interpolation regime, behavioral diversity measures are applicable, but much less effective than those calculated on an independent validation set.

In this paper, we propose a novel ensemble margin based algorithm, which handles imbalanced classification by employing more low margin examples.