Ensemble margin framework for image classification

Ensemble methods have been successfully used as a classification scheme. 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 reliable classifiers. The margin paradigm is at the core of a new ordering-based mislabeled instance elimination method. The same margin framework, relying on an alternative definition of ensemble margin, is used to derive a novel ensemble diversity measure that has the property of revealing sources of diversity at data level. Our work has been successfully applied to image data.