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25 February 2012
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L1-based compression of random forest models
Joly, Arnaud  ; Schnitzler, François  ; Geurts, Pierre  et al.
2012 • In 20th European Symposium on Artificial Neural Networks
Peer reviewed
 

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Keywords :
Ensemble of randomized trees; Pruning; L1-norm regularization; LASSO; Supervised learning; Machine Learning; Randomization; Model reduction; Decision tree
Abstract :
[en] Random forests are effective supervised learning methods applicable to large-scale datasets. However, the space complexity of tree ensembles, in terms of their total number of nodes, is often prohibitive, specially in the context of problems with very high-dimensional input spaces. We propose to study their compressibility by applying a L1-based regularization to the set of indicator functions defined by all their nodes. We show experimentally that preserving or even improving the model accuracy while significantly reducing its space complexity is indeed possible.
Research center :
Système et modélisation
GIGA‐R - Giga‐Research - ULiège
Disciplines :
Electrical & electronics engineering
Computer science
Author, co-author :
Joly, Arnaud ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Schnitzler, François ;  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) > Systèmes et modélisation
Wehenkel, Louis ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Language :
English
Title :
L1-based compression of random forest models
Publication date :
April 2012
Event name :
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Event organizer :
Michel Verleysen
Event place :
Bruges, Belgium
Event date :
25 - 27 April 2012
Audience :
International
Main work title :
20th European Symposium on Artificial Neural Networks
Peer reviewed :
Peer reviewed
Funders :
FRIA - Fonds pour la Formation à la Recherche dans l'Industrie et dans l'Agriculture
Biomagnet IUAP network of the Belgian Science Policy Office
Pascal2 network of excellence of the EC

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