• Publications
  • Influence
Extremely randomized trees
TLDR
This paper proposes a new tree-based ensemble method for supervised classification and regression problems that selects splits, both attribute and cut-point, totally or partially at random. Expand
  • 2,677
  • 272
  • PDF
Tree-Based Batch Mode Reinforcement Learning
TLDR
Reinforcement learning aims to determine an optimal control policy from interaction with a system or from observations gathered from a system. Expand
  • 799
  • 142
  • PDF
Inferring Regulatory Networks from Expression Data Using Tree-Based Methods
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 particularExpand
  • 761
  • 112
  • PDF
Understanding variable importances in forests of randomized trees
TLDR
In this work we characterize the Mean Decrease Impurity (MDI) variable importances as measured by an ensemble of totally randomized trees in asymptotic sample and ensemble size conditions. Expand
  • 441
  • 25
  • PDF
A complete fuzzy decision tree technique
TLDR
In this paper, a new method of fuzzy decision trees called soft decision trees (SDT) is presented. Expand
  • 407
  • 17
  • PDF
Clinical data based optimal STI strategies for HIV: a reinforcement learning approach
TLDR
This paper addresses the problem of computing optimal structured treatment interruption strategies for HIV infected patients from clinical data, without the need of an accurate mathematical model of HIV infection dynamics. Expand
  • 108
  • 16
  • PDF
Random subwindows for robust image classification
TLDR
We present a novel, generic image classification method based on a recent machine learning algorithm and a novel technique of extracting subwindows that are suitably normalized to yield robustness to certain image transformations. Expand
  • 303
  • 14
  • PDF
Automatic Learning Techniques in Power Systems
TLDR
Automatic Learning Techniques in Power Systems is a useful reference source for professionals and researchers developing automatic learning systems in the electrical power field. Expand
  • 295
  • 12
Contingency Filtering Techniques for Preventive Security-Constrained Optimal Power Flow
TLDR
This paper focuses on contingency filtering to accelerate the iterative solution of preventive security-constrained optimal power flow (PSCOPF) problems. Expand
  • 132
  • 10
  • PDF
Supervised learning with decision tree-based methods in computational and systems biology.
TLDR
This paper provides an accessible and comprehensive introduction to decision tree-based supervised learning methods and a survey of their applications in the context of computational and systems biology. Expand
  • 134
  • 9
  • PDF