Building an ensemble classifier using ensemble margin. Application to image classification

Bagging is a simple and powerful ensemble method which relies on bootstrap sampling over training data to produce diversity. Indeed, ensembles generalise better when their members form a diverse and accurate set. In this paper, the margin theory is exploited to select training instances for bagging. The selection of training data is performed using a new iterative guided bagging algorithm exploiting low margin instances. This method has been successfully applied to image data. Results show that low margin instances have a major influence on forming an appropriate training set to build reliable ensemble classifiers, leading to a significant increase in both overall and per-class accuracies.