1 code implementation • NeurIPS 2021 • Antonio Sutera, Gilles Louppe, Van Anh Huynh-Thu, Louis Wehenkel, Pierre Geurts
Random forests have been widely used for their ability to provide so-called importance measures, which give insight at a global (per dataset) level on the relevance of input variables to predict a certain output.
no code implementations • 18 May 2019 • Arnaud Joly, Louis Wehenkel, Pierre Geurts
We consider several extensions of gradient boosting to address such problems.
no code implementations • 4 Sep 2017 • Antonio Sutera, Célia Châtel, Gilles Louppe, Louis Wehenkel, Pierre Geurts
Dealing with datasets of very high dimension is a major challenge in machine learning.
no code implementations • 30 Nov 2016 • Gal Dalal, Elad Gilboa, Shie Mannor, Louis Wehenkel
We devise the Unit Commitment Nearest Neighbor (UCNN) algorithm to be used as a proxy for quickly approximating outcomes of short-term decisions, to make tractable hierarchical long-term assessment and planning for large power systems.
no code implementations • 12 May 2016 • Antonio Sutera, Gilles Louppe, Vân Anh Huynh-Thu, Louis Wehenkel, Pierre Geurts
In many cases, feature selection is often more complicated than identifying a single subset of input variables that would together explain the output.
no code implementations • 24 Apr 2014 • Marie Schrynemackers, Louis Wehenkel, M. Madan Babu, Pierre Geurts
Here, we systematically investigate, theoretically and empirically, the exploitation of tree-based ensemble methods in the context of these two approaches for biological network inference.
no code implementations • 14 Apr 2014 • Arnaud Joly, Pierre Geurts, Louis Wehenkel
We adapt the idea of random projections applied to the output space, so as to enhance tree-based ensemble methods in the context of multi-label classification.
no code implementations • NeurIPS 2013 • Gilles Louppe, Louis Wehenkel, Antonio Sutera, Pierre Geurts
Despite growing interest and practical use in various scientific areas, variable importances derived from tree-based ensemble methods are not well understood from a theoretical point of view.