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On-line Random Forests Amir Saffari, Christian Leistner, Jakob Santner Martin Godec, Horst Bischof Institute for Computer Graphics and Vision Graz University of Technology, Austria October 3, 2009 Introduction On-line Random Forests

  1. On-line Random Forests Amir Saffari, Christian Leistner, Jakob Santner Martin Godec, Horst Bischof Institute for Computer Graphics and Vision Graz University of Technology, Austria October 3, 2009

  2. Introduction On-line Random Forests Experiments Discussions Motivations Random Forest (RF) is an ensemble of random trees. Graz University of Technology Saffari et al. On-line Random Forests

  3. Introduction On-line Random Forests Experiments Discussions Motivations Random Forest (RF) is an ensemble of random trees. RFs achieve state-of-the-art performance in many applications. Graz University of Technology Saffari et al. On-line Random Forests

  4. Introduction On-line Random Forests Experiments Discussions Motivations Random Forest (RF) is an ensemble of random trees. RFs achieve state-of-the-art performance in many applications. It is fast both during the the training and testing phase. Graz University of Technology Saffari et al. On-line Random Forests

  5. Introduction On-line Random Forests Experiments Discussions Motivations Random Forest (RF) is an ensemble of random trees. RFs achieve state-of-the-art performance in many applications. It is fast both during the the training and testing phase. It is easy to implement them in a distributed computing environment or on multi-core CPUs/GPUs. Graz University of Technology Saffari et al. On-line Random Forests

  6. Introduction On-line Random Forests Experiments Discussions Motivations Random Forest (RF) is an ensemble of random trees. RFs achieve state-of-the-art performance in many applications. It is fast both during the the training and testing phase. It is easy to implement them in a distributed computing environment or on multi-core CPUs/GPUs. RFs are inherently multi-class classifiers. Graz University of Technology Saffari et al. On-line Random Forests

  7. Introduction On-line Random Forests Experiments Discussions Motivations Random Forest (RF) is an ensemble of random trees. RFs achieve state-of-the-art performance in many applications. It is fast both during the the training and testing phase. It is easy to implement them in a distributed computing environment or on multi-core CPUs/GPUs. RFs are inherently multi-class classifiers. On-line learning is needed for many applications where the size of the data is huge or the data is available from a stream. Graz University of Technology Saffari et al. On-line Random Forests

  8. Introduction On-line Random Forests Experiments Discussions Decision Trees Graz University of Technology Saffari et al. On-line Random Forests

  9. Introduction On-line Random Forests Experiments Discussions Decision Trees Graz University of Technology Saffari et al. On-line Random Forests

  10. Introduction On-line Random Forests Experiments Discussions Decision Trees Graz University of Technology Saffari et al. On-line Random Forests

  11. Introduction On-line Random Forests Experiments Discussions Decision Trees Graz University of Technology Saffari et al. On-line Random Forests

  12. Introduction On-line Random Forests Experiments Discussions Decision Trees Decision tree is a greedy method which uses a local optimization. Graz University of Technology Saffari et al. On-line Random Forests

  13. Introduction On-line Random Forests Experiments Discussions Decision Trees Decision tree is a greedy method which uses a local optimization. The class of tests could be limited since for finding the best split an optimization step is required. Graz University of Technology Saffari et al. On-line Random Forests

  14. Introduction On-line Random Forests Experiments Discussions Decision Trees Decision tree is a greedy method which uses a local optimization. The class of tests could be limited since for finding the best split an optimization step is required. Decision tree is very sensitive to data noise. Graz University of Technology Saffari et al. On-line Random Forests

  15. Introduction On-line Random Forests Experiments Discussions Ensemble of Bagged Trees L. Breiman (1996) Graz University of Technology Saffari et al. On-line Random Forests

  16. Introduction On-line Random Forests Experiments Discussions Ensemble of Bagged Trees Graz University of Technology Saffari et al. On-line Random Forests

  17. Introduction On-line Random Forests Experiments Discussions Random Forests L. Breiman (2001) Graz University of Technology Saffari et al. On-line Random Forests

  18. Introduction On-line Random Forests Experiments Discussions Elements of On-line Learning Sample ( x , y ) is arriving sequentially from a stream. Graz University of Technology Saffari et al. On-line Random Forests

  19. Introduction On-line Random Forests Experiments Discussions Elements of On-line Learning Sample ( x , y ) is arriving sequentially from a stream. On-line bagging. On-line random tree growing mechanism. Graz University of Technology Saffari et al. On-line Random Forests

  20. Introduction On-line Random Forests Experiments Discussions On-line Bagging Oza and Russell (2001): Draw a random integer: k ∼ Poisson( λ ) Graz University of Technology Saffari et al. On-line Random Forests

  21. Introduction On-line Random Forests Experiments Discussions On-line Bagging Oza and Russell (2001): Draw a random integer: k ∼ Poisson( λ ) If k > 0: Train the model (tree) on ( x , y ) k times. else: Use ( x , y ) to compute the out-of-bag-error and refinement. Graz University of Technology Saffari et al. On-line Random Forests

  22. Introduction On-line Random Forests Experiments Discussions On-line Random Tree Optimizing the structure of a tree on-line is difficult. Graz University of Technology Saffari et al. On-line Random Forests

  23. Introduction On-line Random Forests Experiments Discussions On-line Random Tree Graz University of Technology Saffari et al. On-line Random Forests

  24. Introduction On-line Random Forests Experiments Discussions On-line Random Tree Graz University of Technology Saffari et al. On-line Random Forests

  25. Introduction On-line Random Forests Experiments Discussions On-line Random Tree Graz University of Technology Saffari et al. On-line Random Forests

  26. Introduction On-line Random Forests Experiments Discussions On-line Random Tree Graz University of Technology Saffari et al. On-line Random Forests

  27. Introduction On-line Random Forests Experiments Discussions Temporal Knowledge Weighting In some applications, the distribution of the data is changing over time. Graz University of Technology Saffari et al. On-line Random Forests

  28. Introduction On-line Random Forests Experiments Discussions Temporal Knowledge Weighting In some applications, the distribution of the data is changing over time. Select a tree randomly from { t | t ∈ { 1 , · · · , T } , a t > 1 /γ } . If OOBE t > rand() Discard the t -th tree f t = newTree() Graz University of Technology Saffari et al. On-line Random Forests

  29. Introduction On-line Random Forests Experiments Discussions Machine Learning Datasets We set: T = 200, α = 0 . 1 ∗ N train , β = 0 . 1 For on-line boosting models, we use 50 selectors with 10 decision stumps in each selector and for multi-class datasets we use a 1-vs-all strategy. Code is available at: www.ymer.org/amir/software/online-random-forests Dataset # Train # Test # Class # Feat. Mushrooms 6000 x 20 2124 2 112 DNA 1400 x 20 1186 3 180 SatImage 3104 x 20 2000 6 36 USPS 7291 x 20 2007 10 256 Letter 15000 x 20 5000 26 16 Graz University of Technology Saffari et al. On-line Random Forests

  30. Introduction On-line Random Forests Experiments Discussions Machine Learning Datasets - Results Dataset Off-line RF On-line RF On-line Ada On-line Logit On-line Savage Mushrooms 0 . 010 0 . 012 0 . 013 0 . 012 0 . 013 DNA 0 . 109 0 . 112 0 . 173 0 . 117 0 . 097 SatImage 0 . 113 0 . 118 0 . 257 0 . 152 0 . 156 USPS 0 . 078 0 . 086 0 . 224 0 . 134 0 . 139 Letter 0 . 097 0 . 104 0 . 263 0 . 223 0 . 241 Graz University of Technology Saffari et al. On-line Random Forests

  31. Introduction On-line Random Forests Experiments Discussions Machine Learning Datasets - Results Graz University of Technology Saffari et al. On-line Random Forests

  32. Introduction On-line Random Forests Experiments Discussions Tracking We only use simple Haar-features, without implementing any rotation and scale search and avoid any other engineering methods. We use 100 trees, α = 100, and β = 0 . 1. For the on-line boosting, we use 50 selectors with each 150 features. We evaluate over public datasets: Occluded Face , David Indoor , Sylvester , Rotating Girl . An implementation of the on-line RF on a common NVidia GPU allows an additional 10-times speed up. Graz University of Technology Saffari et al. On-line Random Forests

  33. Introduction On-line Random Forests Experiments Discussions Tracking We only use simple Haar-features, without implementing any rotation and scale search and avoid any other engineering methods. We use 100 trees, α = 100, and β = 0 . 1. For the on-line boosting, we use 50 selectors with each 150 features. We evaluate over public datasets: Occluded Face , David Indoor , Sylvester , Rotating Girl . An implementation of the on-line RF on a common NVidia GPU allows an additional 10-times speed up. Video Graz University of Technology Saffari et al. On-line Random Forests

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