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Context-dependent feature analysis with random forests
Sutera, Antonio; Louppe, Gilles; Huynh-Thu, Vân Anh et al.
2016In Uncertainty In Artificial Intelligence: Proceedings of the Thirty-Two Conference (2016)
Peer reviewed
 

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Keywords :
machine learning; random forest; variable importances
Disciplines :
Computer science
Author, co-author :
Sutera, Antonio ;  Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique
Louppe, Gilles  ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Big Data
Huynh-Thu, Vân Anh ;  Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Wehenkel, Louis  ;  Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Geurts, Pierre ;  Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique
Language :
English
Title :
Context-dependent feature analysis with random forests
Publication date :
June 2016
Event name :
Conference on Uncertainty in Artificial Intelligence 2016
Event place :
Jersey City, United States - New Jersey
Event date :
June 25-29 2016
Audience :
International
Main work title :
Uncertainty In Artificial Intelligence: Proceedings of the Thirty-Two Conference (2016)
Peer reviewed :
Peer reviewed
Commentary :
In many cases, feature selection is often more complicated than identifying a single subset of input variables that would together explain the output. There may be interactions that depend on contextual information, i.e., variables that reveal to be relevant only in some specific circumstances. In this setting, the contribution of this paper is to extend the random forest variable importances framework in order (i) to identify variables whose relevance is context-dependent and (ii) to characterize as precisely as possible the effect of contextual information on these variables. The usage and the relevance of our framework for highlighting context-dependent variables is illustrated on both artificial and real datasets.
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