automatic learning; robust supervised learning methods; time-series classification; learning of optimal control policies
Abstract :
[en] In this paper we present a new tree-based ensemble method called “Extra-Trees”. This algorithm averages predictions of trees obtained by partitioning the inputspace with randomly generated splits, leading to significant improvements of precision, and various algorithmic advantages, in particular reduced computational complexity and scalability. We also discuss two generic applications of this algorithm, namely for time-series classification and for the automatic inference of near-optimal sequential decision policies from experimental data.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Wehenkel, Louis ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Ernst, Damien ; 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
Language :
English
Title :
Ensembles of extremely randomized trees and some generic applications
Publication date :
2006
Event name :
Robust Methods for Power System State Estimation and Load Forecasting
Event place :
Versailles (RTE Building), France
Event date :
2006
Audience :
International
Main work title :
Proceedings of Robust Methods for Power System State Estimation and Load Forecasting
Peer reviewed :
Peer reviewed
Funders :
F.R.S.-FNRS - Fonds de la Recherche Scientifique [BE]
