Reference : Dual Perturb and Combine Algorithm

	Document type :	Scientific congresses and symposiums : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/2268/25740

Title :	Dual Perturb and Combine Algorithm
Language :	English
Author, co-author :	Geurts, Pierre [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Publication date :	2001
Main document title :	Proceedings of AISTATS 2001, Eighth International Workshop on Artificial Intelligence and Statistics
Pages :	196-201
On invitation :	No
Audience :	International
City :	Key-West, Florida
Event name :	Eighth International Workshop on Artificial Intelligence and Statistics
Event date :	January 2001
Event place (city) :	Key-West
Event country :	USA
Keywords :	[en] Machine Learning
Abstract :	[en] In this paper, a dual perturb and combine algorithm is proposed which consists in producing the perturbed predictions at the prediction stage using only one model. To this end, the attribute vector of a test case is perturbed several times by an additive random noise, the model is applied to each of these perturbed vectors and the resulting predictions are aggregated. An analytical version of this algorithm is described in the context of decision tree induction. From experiments on several datasets, it appears that this simple algorithm yields significant improvements on several problems, sometimes comparable to those obtained with bagging. When combined with decision tree bagging, this algorithm also improves accuracy in many problems.
Permalink :	http://hdl.handle.net/2268/25740
Other URL :	http://www.montefiore.ulg.ac.be/services/stochastic/pubs/2001/Geu01

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	Fulltext file(s): File Commentary Version Size Access Open access geurts-aistats2001.pdf Publisher postprint 205.77 kB View/Open

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