Extremely randomized trees

Summary

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.

Keywords

Robustness (evolution), Mathematics, Artificial intelligence, Computer science, Algorithm

References

Stacked generalizationDavid H. Wolpert — 1992
Machine Learning Bias, Statistical Bias, and Statistical Variance of Decision Tree AlgorithmsThomas G. Dietterich, Eun Hui Bae — 2008
C4.5: Programs for Machine Learning (書評)重郎金田 — 1995
The Elements of Statistical Learning: Data Mining, Inference, and PredictionTrevor Hastie, J. Friedman, Robert Tibshirani — 2001
Automatic Learning Techniques in Power SystemsLouis Wehenkel — 1997
Contributions to decision tree induction: bias/variance tradeoff and time series classificationPierre Geurts — 2002
On uncertainty measures used for decision tree inductionLouis Wehenkel — 1996
Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble MethodsGiorgio Valentini, Thomas G. Dietterich — 2004
Approximate Splitting for Ensembles of Trees using HistogramsChandrika Kamath, Erick Cantú‐Paz, David Littau — 2001
A generic approach for image classification based on decision tree ensembles and local sub-windowsRaphaël Marée, Pierre Geurts, Justus Piater — 2004
Randomizing Outputs to Increase Prediction AccuracyLeo Breiman — 2000
Bayes Point MachinesRalf Herbrich, Th Graepel, I C G Campbell — 2001
Classification and regression treesAlexander Gordon, Leo Breiman, Jerome H. Friedman — 1983
An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and RandomizationThomas G. Dietterich — 2000
Variance and Bias for General Loss FunctionsGareth James — 2003
On Bias, Variance, 0/1—Loss, and the Curse-of-DimensionalityJerome H. Friedman — 2004
A Further Comparison of Splitting Rules for Decision-Tree InductionWray Buntine, Tim Niblett — 1992
A decision-theoretic generalization of on-line learning and an application to boostingYoav Freund, Robert E. Schapire — 1997
An Empirical Comparison of Selection Measures for Decision-Tree InductionJohn Mingers — 1989
Random Forests and Adaptive Nearest NeighborsYi Lin, Yongho Jeon — 2006

Cited by

Parallel tree-ensemble algorithms for GPUs using CUDAKarl Jansson, Håkan Sundell, Henrik Boström — 2013
Principal Component Regression Predicts Functional Responses across IndividualsBertrand Thirion, Gaël Varoquaux, Olivier Grisel — 2014
3D Object Retrieval via Range Image Queries based on SIFT descriptors on Panoramic ViewsKonstantinos Sfikas, Ioannis Pratikakis, T. Theoharis — 2012
Forest of Fuzzy Decision Trees and Their Application in Video MiningMarcin Detyniecki, Christophe Marsala, Marie Curie-paris — 2007
Improvements to random forest methodologyRuo Xu — 2013
Machine learning techniques to assess the performance of a gait analysis systemSébastien Pierard, Rémy Phan-Ba, Marc Van Droogenbroeck — 2014
Classifiers for Ischemic Stroke Lesion Segmentation: A Comparison StudyAlexander Makedon — 2015
Un nouvel algorithme de forêts aléatoires d'arbres obliques particulièrement adapté à la classification de données en grandes dimensionsThanh Nghị, Stéphane Lallich, Nguyen-Khang Pham — 2009
eToxPred: a machine learning-based approach to estimate the toxicity of drug candidatesJosé Antonio Fernández Ordóñez — 2019
Towards computerized diagnosis of neurological stance disorders: data mining and machine learning of posturography and swayFelix Mendelssohn Bartholdy — 2019
Image Representations for Ranking and Classification. (Représentations d'images pour la recherche et la classification d'images)Josip Krapac — 2011
Mélanges sous-quadratiques d'arbres de Markov pour l'estimation de la densité de probabilitéSourour Ammar, Philippe Leray, Louis Wehenkel — 2010
Identifying Novel Subtypes of Functional Gastrointestinal Disorder by Analyzing Nonlinear Structure in Integrative Biopsychosocial Questionnaire Data修大西, 康大井, 裕横山 — 2024
Visual Concept Detection and Real Time Object DetectionRan Tao — 2011
Multi-label feature selection with application to musical instrument recognitionTrudie Sandrock — 2013
Modeling and Control of VSC-HVDC TransmissionsH.F. Latorre — 2011
Supervised algorithm selection for flow and other computer vision problemsOisin Mac Aodha — 2014
Photometric classification of quasars from RCS-2 using Random ForestDaniela Carrasco, L. Felipe Barrientos, Karim Pichara — 2014
Adaptive Treatment of Epilepsy via Batch-mode Reinforcement LearningArthur Guez, Robert D. Vincent, Massimo Avoli — 2008
Referential Translation Machines for Predicting Translation QualityErgun Biçici, Andy Way — 2014

Related papers

Researching robustness of information system for measuring of microcontrollers average power consumptionOleksandr Osolinskiy, Орест Кочан, W. Winiecki — 2017
Development and Design Optimization of 2Y Hexarotor with Robustness against Rotor FailureShunsuke Mochida, Remma Matsuda, Tatsuya Ibuki — 2020
Robustness assessment of complex networks using the idle networkMarcus Engsig, Alejandro Tejedor, Yamir Moreno — 2022
Robustness evaluation in analytical methods optimized using experimental designsSérgio L.C. Ferreira, Adriana O. Caires, Thaíse da Silva Borges — 2017
Robust resource loading for engineer-to-order manufacturingGerhard Wullink, EW Hans, van A Aart Harten — 2004
Robust signal detection with nonstandard decision regionsManruth Srisook, D.R. Halverson, Michael Thompson — 1999
Robustness measurement for scalable switch fabricShutao Sun — 2012
The Decision Tree Algorithm Use in Supervised Machine LearningN. Salame — 2023
Heart Disease Prediction using Hybrid machine Learning ModelM. Kavitha, G. Gnaneswar, R. Dinesh — 2021
Machine Learning based Advanced Crime Prediction and AnalysisSameya Khatun, Kavyasree Banoth, Akshara Dilli — 2023