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21 September 2014
Paper published in a book (Scientific congresses and symposiums)
Random forests with random projections of the output space for high dimensional multi-label classification
Joly, Arnaud  ; Geurts, Pierre  ; Wehenkel, Louis 
2014 • In Machine Learning and Knowledge Discovery in Databases
Peer reviewed
 

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Keywords :
Machine learning; Multilabel; Random forest; Random projections
Abstract :
[en] We adapt the idea of random projections applied to the out- put space, so as to enhance tree-based ensemble methods in the context of multi-label classification. We show how learning time complexity can be reduced without affecting computational complexity and accuracy of predictions. We also show that random output space projections may be used in order to reach different bias-variance tradeoffs, over a broad panel of benchmark problems, and that this may lead to improved accuracy while reducing significantly the computational burden of the learning stage.
Research center :
Systems and Modeling Research Unit
Disciplines :
Computer science
Author, co-author :
Joly, Arnaud ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Geurts, Pierre ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique
Wehenkel, Louis ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Language :
English
Title :
Random forests with random projections of the output space for high dimensional multi-label classification
Publication date :
15 September 2014
Event name :
7th European machine learning and data mining conference (ECML-PKDD 2014)
Event place :
Nancy, France
Event date :
From 15 September au 19 September 2014
Audience :
International
Main work title :
Machine Learning and Knowledge Discovery in Databases
Peer reviewed :
Peer reviewed
Funders :
F.R.S.-FNRS - Fonds de la Recherche Scientifique
PASCAL2
IUAP DYSCO
CÉCI - Consortium des Équipements de Calcul Intensif
Commentary :
Source code is available at https://github.com/arjoly/random-output-trees in bsd license

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