A combination of methods for building ensembles of classifiers


In this paper we make an extensive study of different methods for building ensembles of classifiers. We examine variants of ensemble methods that are based on perturbing features. We illustrate the power of using these variants by applying them to a number of different problems. We find that the best performing ensemble is obtained by combining an approach based on random subspace with a cluster-based input decimated ensemble and the principal direction oracle. Compared with other state-of-the-art stand-alone classifiers and ensembles, this method consistently performed well across twelve diverse benchmark datasets. Another useful finding is that this approach does not require parameters to be carefully tuned for each dataset (in contrast to the fundamental importance of parameters tuning when using SVM and extreme learning machines), making our ensemble method well suited for practitioners since there is less risk of over-training. Another interesting finding is that random subspace can be coupled with several other ensemble methods to improve performance.


Information Technology and Cybersecurity

Document Type

Conference Proceeding


f input decimated ensembles, Multiclassifier systems, Pattern classification, Random subspace

Publication Date


Journal Title

Proceedings of the 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012