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Searching for authors named "Pierre Geurts" – sorted by Relevance.

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  • Dual Perturb and Combine Algorithm  
  • by Pierre Geurts — 2001 — Proc. of the Eighth International Workshop on Artificial Intelligence and Statistics (pp. 196– 201). Key-West
  • …In this paper, a dual perturb and combine algorithm is proposed which consists in producing the perturbed predictions at the prediction stage using only one model. To this end, the attribute vector of a test case is perturbed several times by an additive random noise, the model is applied to each of…
  • Cited by 1 (1 self) – Add To MetaCart
  • Some Enhancements of Decision Tree Bagging  
  • by Pierre Geurts — 2000 — Proc. of the 4th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD-2000
  • …This paper investigates enhancements of decision tree bagging which mainly aim at improving computation times, but also accuracy. The three questions which are reconsidered are: discretization of continuous attributes, tree pruning, and sampling schemes. A very simple discretization procedure is pro…
  • Cited by 2 (1 self) – Add To MetaCart
  • Pattern Extraction for Time Series Classification  
  • by Pierre Geurts — 2001
  • …In this paper, we propose some new tools to allow machine learning classifiers to cope with time series data. We first argue that many time-series classification problems can be solved by detecting and combining local properties or patterns in time series. Then, a technique is proposed to find patte…
  • Cited by 31 (2 self) – Add To MetaCart
  • Investigation and Reduction of Discretization Variance in Decision tree Induction  
  • by Pierre Geurts, Louis Wehenkel — 2000 — Proc. of the 11th European Conference on Machine Learning (ECML-2000
  • …This paper focuses on the variance introduced by the discretization techniques used to handle continuous attributes in decision tree induction. Different discretization procedures are first studied empirically, then means to reduce the discretization variance are proposed. The experiment shows that …
  • Cited by 4 (3 self) – Add To MetaCart
  • Segment and combine approach for non-parametric time-series classification  
  • by Pierre Geurts, Louis Wehenkel — 2005 — in Proceedings of the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD
  • …Abstract. This paper presents a novel, generic, scalable, autonomous, and flexible supervised learning algorithm for the classification of multivariate and variable length time series. The essential ingredients of the algorithm are randomization, segmentation of time-series, decision tree ensemble b…
  • Cited by 7 (4 self) – Add To MetaCart
  • unknown title  
  • by Minh Quach, Pierre Geurts
  • …Elucidating the structure of genetic regulatory networks: a study of a second order dynamical model on artificial data…
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  • Extremely Randomized Trees  
  • by Pierre Geurts June — 2003 — Machine Learning
  • …This paper presents a new learning algorithm based on decision tree ensembles. In opposition to the classical decision tree induction method, the trees of the ensemble are built by selecting the tests during their induction fully at random.…
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  • Biomedical image classification with random subwindows and decision trees  
  • by Raphaël Marée, Pierre Geurts, Justus Piater, Louis Wehenkel — 2005 — Proc. ICCV workshop on Computer Vision for Biomedical Image Applications
  • …Abstract. In this paper, we address a problem of biomedical image classification that involves the automatic classification of x-ray images in 57 predefined classes with large intra-class variability. To achieve that goal, we apply and slightly adapt a recent generic method for image classification …
  • Cited by 4 (2 self) – Add To MetaCart
  • Decision trees and random subwindows for object recognition  
  • by Raphaël Marée, Pierre Geurts, Justus Piater, Louis Wehenkel — 2005 — In ICML workshop on Machine Learning Techniques for Processing Multimedia Content (MLMM2005
  • …In this paper, we compare five tree-based machine learning methods within our recent generic image-classification framework based on random extraction and classification of subwindows. We evaluate them on three publicly available object-recognition datasets (COIL-100, ETH-80, and ZuBuD). Our compari…
  • Cited by 2 (1 self) – Add To MetaCart
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