Reference : L1-based compression of random forest models
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Computer science
Engineering, computing & technology : Electrical & electronics engineering
http://hdl.handle.net/2268/112824
L1-based compression of random forest models
English
Joly, Arnaud mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Schnitzler, François 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 >]
Wehenkel, Louis mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Apr-2012
20th European Symposium on Artificial Neural Networks
Yes
No
International
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
25 - 27 April 2012
Michel Verleysen
Bruges
Belgium
[en] Ensemble of randomized trees ; Pruning ; L1-norm regularization ; LASSO ; Supervised learning ; Machine Learning ; Randomization ; Model reduction ; Decision tree
[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.
Système et modélisation ; GIGA-research
Fonds pour la formation à la Recherche dans l'Industrie et dans l'Agriculture (Communauté française de Belgique) - FRIA ; Biomagnet IUAP network of the Belgian Science Policy Office ; Pascal2 network of excellence of the EC
Researchers ; Professionals
http://hdl.handle.net/2268/112824

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