Reference : Ensembles on Random Patches
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/130099
Ensembles on Random Patches
English
Louppe, Gilles mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Geurts, Pierre mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
2012
Machine Learning and Knowledge Discovery in Databases
Springer-Verlag
Lecture Notes in Computer Science, Vol. 7523
Yes
No
International
978-3-642-33459-7
Berlin
European Conference on Machine Learning (ECML 2012)
From 24/09/2012 to 28/09/2012
Prof. Peter Flach
Prof. Tijl De Bie
Prof. Nello Cristianini
Bristol
United Kingdoms
[en] ensemble methods ; large-scale learning ; supervised learning
[en] In this paper, we consider supervised learning under the assumption that the available memory is small compared to the dataset size. This general framework is relevant in the context of big data, distributed databases and embedded systems. We investigate a very simple, yet effective, ensemble framework that builds each individual model of the ensemble from a random patch of data obtained by drawing random subsets of both instances and features from the whole dataset. We carry out an extensive and systematic evaluation of this method on 29 datasets, using decision tree-based estimators. With respect to popular ensemble methods, these experiments show that the proposed method provides on par performance in terms of accuracy while simultaneously lowering the memory needs, and attains significantly better performance when memory is severely constrained.
Researchers
http://hdl.handle.net/2268/130099
The original publication is available at www.springerlink.com.

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