Consistency of Online Random Forests

As a testament to their success, the theory of random forests has long been outpaced by their application in practice. In this paper, we take a step towards narrowing this gap by providing a consistency result for online random forests.

[1]  Udaya B. Kogalur,et al.  Consistency of Random Survival Forests. , 2008, Statistics & probability letters.

[2]  László Györfi,et al.  A Probabilistic Theory of Pattern Recognition , 1996, Stochastic Modelling and Applied Probability.

[3]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[4]  D. R. Cutler,et al.  Utah State University From the SelectedWorks of , 2017 .

[5]  Thomas Seidl,et al.  MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering , 2010, WAPA.

[6]  João Gama,et al.  Learning decision trees from dynamic data streams , 2005, SAC '05.

[7]  Antonio Criminisi,et al.  Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning , 2012, Found. Trends Comput. Graph. Vis..

[8]  Horst Bischof,et al.  On-line Random Forests , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[9]  Geoff Hulten,et al.  Mining high-speed data streams , 2000, KDD '00.

[10]  David B. Skillicorn,et al.  Streaming Random Forests , 2007, 11th International Database Engineering and Applications Symposium (IDEAS 2007).

[11]  L. Breiman.  CONSISTENCY FOR A SIMPLE MODEL OF RANDOM FORESTS , 2004 .

[12]  Gérard Biau,et al.  Analysis of a Random Forests Model , 2010, J. Mach. Learn. Res..

[13]  Robert P. Sheridan,et al.  Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling , 2003, J. Chem. Inf. Comput. Sci..

[14]  A. Prasad,et al.  Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction , 2006, Ecosystems.

[15]  Robin Genuer,et al.  Variance reduction in purely random forests , 2012 .

[16]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[17]  Andrew W. Fitzgibbon,et al.  Real-time human pose recognition in parts from single depth images , 2011, CVPR 2011.

[18]  Nicolai Meinshausen,et al.  Quantile Regression Forests , 2006, J. Mach. Learn. Res..

[19]  Robin Genuer,et al.  Risk bounds for purely uniformly random forests , 2010, 1006.2980.

[20]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[21]  Stuart J. Russell,et al.  Online bagging and boosting , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[22]  Yoav Freund,et al.  Boosting: Foundations and Algorithms , 2012 .

[23]  Graham Cormode,et al.  An improved data stream summary: the count-min sketch and its applications , 2004, J. Algorithms.

[24]  Geoff Holmes,et al.  New ensemble methods for evolving data streams , 2009, KDD.

[25]  Andrew Zisserman,et al.  Image Classification using Random Forests and Ferns , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[26]  Raghu Ramakrishnan,et al.  Proceedings : KDD 2000 : the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 20-23, 2000, Boston, MA, USA , 2000 .

[27]  Geoff Holmes,et al.  Ensembles of Restricted Hoeffding Trees , 2012, TIST.

[28]  Yi Lin,et al.  Random Forests and Adaptive Nearest Neighbors , 2006 .

[29]  Luc Devroye,et al.  Consistency of Random Forests and Other Averaging Classifiers , 2008, J. Mach. Learn. Res..