#### Filter Results:

- Full text PDF available (104)

#### Publication Year

2000

2017

- This year (6)
- Last 5 years (45)
- Last 10 years (82)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Brain Region

#### Key Phrases

#### Method

#### Organism

Learn More

- 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 (x t , u t , r t , x t+1) where x t denotes the system state at time t, u t the control action taken, r t 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)

- 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)

- 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)

- Pierre Geurts
- PKDD
- 2001

In this paper, we propose some new tools to allow machine learning classiiers to cope with time series data. We rst argue that many time-series classiication problems can be solved by detecting and combining local properties or patterns in time series. Then, a technique is proposed to nd patterns which are useful for classiication. These patterns are… (More)

- Yongjun Liao, Wei Du, Pierre Geurts, Guy Leduc
- CoNEXT
- 2011

In large-scale networks, full-mesh active probing of end-to-end performance metrics is infeasible. Measuring a small set of pairs and predicting the others is more scalable. Under this framework, we formulate the prediction problem as matrix completion, whereby unknown entries of an incomplete matrix of pairwise measurements are to be predicted. This… (More)

- Gilles Louppe, Louis Wehenkel, Antonio Sutera, Pierre Geurts
- NIPS
- 2013

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. In this work we characterize the Mean Decrease Impurity (MDI) variable importances as measured by an ensemble of totally ran-domized trees in asymptotic sample and… (More)

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)

- Marie Schrynemackers, Robert Küffner, Pierre Geurts
- Front. Genet.
- 2013

Networks provide a natural representation of molecular biology knowledge, in particular to model relationships between biological entities such as genes, proteins, drugs, or diseases. Because of the effort, the cost, or the lack of the experiments necessary for the elucidation of these networks, computational approaches for network inference have been… (More)