Learning with Ensembles of Randomized Trees : New Insights

@inproceedings{Pisetta2010LearningWE,
  title={Learning with Ensembles of Randomized Trees : New Insights},
  author={Vincent Pisetta and Pierre-Emmanuel Jouve and D. Zighed},
  booktitle={ECML/PKDD},
  year={2010}
}
Ensembles of randomized trees such as Random Forests are among the most popular tools used in machine learning and data mining. Such algorithms work by introducing randomness in the induction of several decision trees before employing a voting scheme to give a prediction for unseen instances. In this paper, randomized trees ensembles are studied in the point of view of the basis functions they induce. We point out a connection with kernel target alignment, a measure of kernel quality, which… Expand
Improvement of randomized ensembles of trees for supervised learning in very high dimension
Tree-based ensemble methods, such as random forests and extremely randomized trees, are methods of choice for handling high dimensional problems. One important drawback of these methods however isExpand
On Oblique Random Forests
TLDR
This work proposes to employ "oblique" random forests (oRF) built from multivariate trees which explicitly learn optimal split directions at internal nodes using linear discriminative models, rather than using random coefficients as the original oRF. Expand
Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article]
TLDR
This paper reviews traditional as well as state-of-the-art ensemble methods and thus can serve as an extensive summary for practitioners and beginners. Expand
An Ensemble Learning Based Framework for Traditional Chinese Medicine Data Analysis with ICD-10 Labels
TLDR
The proposed ensemble model can effectively model the implied knowledge and experience in historic clinical data records and the computational cost of training a set of base learners is relatively low. Expand
Artificial Intelligence in Stochastic Multiple Criteria Decision Making
TLDR
This chapter presents the concept of stochastic multiple criteria decision making (MCDM) method to solve complex ranking decision problems and different ways of improvements of these complex systems are proposed. Expand

References

SHOWING 1-10 OF 37 REFERENCES
Extremely randomized trees
TLDR
A new tree-based ensemble method for supervised classification and regression problems that consists of randomizing strongly both attribute and cut-point choice while splitting a tree node and builds totally randomized trees whose structures are independent of the output values of the learning sample. Expand
An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization
TLDR
The experiments show that in situations with little or no classification noise, randomization is competitive with (and perhaps slightly superior to) bagging but not as accurate as boosting, and sometimes better than randomization. Expand
Random Forests
  • L. Breiman
  • Mathematics, Computer Science
  • Machine Learning
  • 2004
TLDR
Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression. Expand
PERT – Perfect Random Tree Ensembles
Ensemble classifiers originated in the machine learning community. They work by fitting many individual classifiers and combining them by weighted or unweighted voting. The ensemble classifier isExpand
Support Vector Machinery for Infinite Ensemble Learning
TLDR
An infinite ensemble learning framework based on the support vector machine (SVM) is formulated that can output an infinite and nonsparse ensemble through embedding infinitely many hypotheses into an SVM kernel and is used to derive two novel kernels, the stump kernel and the perceptron kernel. Expand
Experiments with a New Boosting Algorithm
TLDR
This paper describes experiments carried out to assess how well AdaBoost with and without pseudo-loss, performs on real learning problems and compared boosting to Breiman's "bagging" method when used to aggregate various classifiers. Expand
SOME INFINITY THEORY FOR PREDICTOR ENSEMBLES
To dispel some of the mystery about what makes tree ensembles work, they are looked at in distribution space i.e. the limit case of "infinite" sample size. It is shown that the simplest kind of treesExpand
Spectrum of Variable-Random Trees
In this paper, we show that a continuous spectrum of randomisation exists, in which most existing tree randomisations are only operating around the two ends of the spectrum. That leaves a huge partExpand
Consistency of Random Forests and Other Averaging Classifiers
TLDR
A number of theorems are given that establish the universal consistency of averaging rules, and it is shown that some popular classifiers, including one suggested by Breiman, are not universally consistent. Expand
Boosting as a Regularized Path to a Maximum Margin Classifier
TLDR
It is built on recent work by Efron et al. to show that boosting approximately (and in some cases exactly) minimizes its loss criterion with an l1 constraint on the coefficient vector, and shows that as the constraint is relaxed the solution converges (in the separable case) to an "l1-optimal" separating hyper-plane. Expand
...
1
2
3
4
...