Building ensembles of adaptive nested dichotomies with random-pair selection
Leathart, T., Pfahringer, B., & Frank, E. (2016). Building ensembles of adaptive nested dichotomies with random-pair selection. In P. Frasconi, N. Landwehr, G. Manco, & J. Vreeken (Eds.), Proceedings of Joint European Conference on Machine Learning and Knowledge Discovery in Databases (Vol. Part II, LNAI 9852, pp. 179–194). Cham, Switzerland: Springer. http://doi.org/10.1007/978-3-319-46227-1_12
Permanent Research Commons link: https://hdl.handle.net/10289/10767
A system of nested dichotomies is a method of decomposing a multi-class problem into a collection of binary problems. Such a system recursively applies binary splits to divide the set of classes into two subsets, and trains a binary classifier for each split. Although ensembles of nested dichotomies with random structure have been shown to perform well in practice, using a more sophisticated class subset selection method can be used to improve classification accuracy. We investigate an approach to this problem called random-pair selection, and evaluate its effectiveness compared to other published methods of subset selection. We show that our method outperforms other methods in many cases when forming ensembles of nested dichotomies, and is at least on par in all other cases. The software related to this paper is available at https://svn.cms.waikato.ac.nz/svn/weka/trunk/packages/ internal/ensemblesOfNestedDichotomies/.
This is an author’s accepted version of an article published in the Proceedings of Joint European Conference on Machine Learning and Knowledge Discovery in Databases. © Springer International Publishing AG 2016.