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Dual Perturb and Combine Algorithm
Geurts, Pierre
2001In Proceedings of AISTATS 2001, Eighth International Workshop on Artificial Intelligence and Statistics
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Keywords :
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.
Disciplines :
Computer science
Author, co-author :
Geurts, Pierre ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Language :
English
Title :
Dual Perturb and Combine Algorithm
Publication date :
2001
Event name :
Eighth International Workshop on Artificial Intelligence and Statistics
Event place :
Key-West, United States
Event date :
January 2001
Audience :
International
Main work title :
Proceedings of AISTATS 2001, Eighth International Workshop on Artificial Intelligence and Statistics
Pages :
196-201
Peer reviewed :
Peer reviewed
Available on ORBi :
since 15 October 2009

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