International audienceThe ensemble methods are popular machine learning techniques which are powerful when one wants to deal with both classification or prediction problems. A set of classifiers (regression or classification trees) is constructed, and the classification or the prediction of a new data instance is done by tacking a weighted vote. A tree is a piece-wise constant estimator on partitions obtained from the data. These partitions are induced by recursive dyadic split of the set of input variables. For example, CART (Classification And Regression Trees) [1] is an efficient algorithm for the construction of a tree. The goal is to partition the space of input variable values in the most as possible "homogeneous" K disjoint regions. ...
Uncertainty was introduced to chemical descriptors of 16 publicly available data sets to various deg...
An ensemble is viewed as a machine learning system that combines multiple models to work collectivel...
Dans de nombreux problèmes en apprentissage supervisé, les entrées ont une structure de groupes conn...
none2One of the current challenges in the field of data mining is to develop techniques to analyze u...
Publication arXiv, travail de recherche postdoctoral sur les arbres de décision probabilistesTree-ba...
AbstractThis paper addresses the classification problem with imperfect data. More precisely, it exte...
Decision trees estimate prediction certainty using the class distribution in the leaf responsible fo...
We propose a robust decision tree induction method that mitigates the problems of instability and p...
Abstract — Classification is one of the important data mining techniques and Decision Tree is a most...
Traditional decision tree classifiers work with data whose values are known and precise. We extend s...
ABSTRACT Classification is a classical problem in machine learning and data mining. One of the most ...
Decision trees are among the most effective and interpretable classification algorithms while ensemb...
171 pagesMachine learning has become ubiquitous in many areas, including high-stake applications suc...
Copyright © 2004 Springer-Verlag Berlin Heidelberg. The final publication is available at link.sprin...
Uncertainty measures estimate the reliability of a predictive model. Especially in the field of mole...
Uncertainty was introduced to chemical descriptors of 16 publicly available data sets to various deg...
An ensemble is viewed as a machine learning system that combines multiple models to work collectivel...
Dans de nombreux problèmes en apprentissage supervisé, les entrées ont une structure de groupes conn...
none2One of the current challenges in the field of data mining is to develop techniques to analyze u...
Publication arXiv, travail de recherche postdoctoral sur les arbres de décision probabilistesTree-ba...
AbstractThis paper addresses the classification problem with imperfect data. More precisely, it exte...
Decision trees estimate prediction certainty using the class distribution in the leaf responsible fo...
We propose a robust decision tree induction method that mitigates the problems of instability and p...
Abstract — Classification is one of the important data mining techniques and Decision Tree is a most...
Traditional decision tree classifiers work with data whose values are known and precise. We extend s...
ABSTRACT Classification is a classical problem in machine learning and data mining. One of the most ...
Decision trees are among the most effective and interpretable classification algorithms while ensemb...
171 pagesMachine learning has become ubiquitous in many areas, including high-stake applications suc...
Copyright © 2004 Springer-Verlag Berlin Heidelberg. The final publication is available at link.sprin...
Uncertainty measures estimate the reliability of a predictive model. Especially in the field of mole...
Uncertainty was introduced to chemical descriptors of 16 publicly available data sets to various deg...
An ensemble is viewed as a machine learning system that combines multiple models to work collectivel...
Dans de nombreux problèmes en apprentissage supervisé, les entrées ont une structure de groupes conn...