A Further Comparison of Splitting Rules for Decision-Tree Induction
Machine Learning
Original Contribution: Stacked generalization
Neural Networks
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
On the Algorithmic Implementation of Stochastic Discrimination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Machine Learning
C4.5: Programs for Machine Learning
C4.5: Programs for Machine Learning
Linear Programming Boosting via Column Generation
Machine Learning
Boosting as a Regularized Path to a Maximum Margin Classifier
The Journal of Machine Learning Research
Machine Learning
Support Vector Machinery for Infinite Ensemble Learning
The Journal of Machine Learning Research
Consistency of Random Forests and Other Averaging Classifiers
The Journal of Machine Learning Research
Spectrum of variable-random trees
Journal of Artificial Intelligence Research
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
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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 suggests that randomization is a way to obtain a high alignment, leading to possibly low generalization error. The connection also suggests to post-process ensembles with sophisticated linear separators such as Support Vector Machines (SVM). Interestingly, post-processing gives experimentally better performances than a classical majority voting. We finish by comparing those results to an approximate infinite ensemble classifier very similar to the one introduced by Lin and Li. This methodology also shows strong learning abilities, comparable to ensemble post-processing.