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- Pierre Geurts, Damien Ernst, Louis Wehenkel
- Machine Learning
- 2006

This paper proposes a new tree-based ensemble method for supervised classification and regression problems. It essentially consists of randomizing strongly both attribute and cut-point choice while splitting a tree node. In the extreme case, it builds totally randomized trees whose structures are independent of the output values of the learning sample. The… (More)

- Damien Ernst, Pierre Geurts, Louis Wehenkel
- Journal of Machine Learning Research
- 2005

Reinforcement learning aims to determine an optimal control policy from interaction with a system or from observations gathered from a system. In batch mode, it can be achieved by approximating the so-called Q-function based on a set of four-tuples (xt ,ut ,rt ,xt+1) where xt denotes the system state at time t, ut the control action taken, rt the… (More)

- Vân Anh Huynh-Thu, Alexandre Irrthum, Louis Wehenkel, Pierre Geurts
- PloS one
- 2010

One of the pressing open problems of computational systems biology is the elucidation of the topology of genetic regulatory networks (GRNs) using high throughput genomic data, in particular microarray gene expression data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge aims to evaluate the success of GRN inference algorithms… (More)

- Raphaël Marée, Pierre Geurts, Justus H. Piater, Louis Wehenkel
- 2005 IEEE Computer Society Conference on Computer…
- 2005

We present a novel, generic image classification method based on a recent machine learning algorithm (ensembles of extremely randomized decision trees). Images are classified using randomly extracted subwindows that are suitably normalized to yield robustness to certain image transformations. Our method is evaluated on four very different, publicly… (More)

- Yongjun Liao, Pierre Geurts, Guy Leduc
- Networking
- 2010

Network Coordinate Systems (NCS) are promising techniques to predict unknown network distances from a limited number of measurements. Most NCS algorithms are based on metric space embedding and suffer from the inability to represent distance asymmetries and Triangle Inequality Violations (TIVs). To overcome these drawbacks, we formulate the problem of… (More)

- Yongjun Liao, Wei Du, Pierre Geurts, Guy Leduc
- IEEE/ACM Transactions on Networking
- 2013

The knowledge of end-to-end network distances is essential to many Internet applications. As active probing of all pairwise distances is infeasible in large-scale networks, a natural idea is to measure a few pairs and to predict the other ones without actually measuring them. This paper formulates the prediction problem as matrix completion where the… (More)

- Pierre Geurts
- PKDD
- 2001

In this paper we explain how to design intelligent agents able to process the information acquired from interaction with a system to learn a good control policy and show how the methodology can be applied to control some devices aimed to damp electrical power oscillations. The control problem is formalized as a discrete-time optimal control problem and the… (More)

- Pierre Geurts, Louis Wehenkel, Florence d'Alché-Buc
- ICML
- 2006

We extend tree-based methods to the prediction of structured outputs using a kernelization of the algorithm that allows one to grow trees as soon as a kernel can be defined on the output space. The resulting algorithm, called output kernel trees (OK3), generalizes classification and regression trees as well as tree-based ensemble methods in a principled… (More)

- Pierre Geurts, Nizar Touleimat, Marie Dutreix, Florence d'Alché-Buc
- BMC Bioinformatics
- 2007

Elucidating biological networks between proteins appears nowadays as one of the most important challenges in systems biology. Computational approaches to this problem are important to complement high-throughput technologies and to help biologists in designing new experiments. In this work, we focus on the completion of a biological network from various… (More)