Incremental learning extremely random forest classifier for online learning

Ruan Jian Xue Bao/Journal of Software

Volumn 22, Issue 9, 2011, Pages 2059-2074

  Wang, Ai Ping ^a   Wan, Guo Wei ^a   Cheng, Zhi Quan ^a   Li, Si Kun ^a  

^a NATIONAL UNIVERSITY OF DEFENSE TECHNOLOGY   (China)

Author keywords

Extremely random forest classifier; Incremental learning; Online learning

Indexed keywords

GINI INDEX; INCREMENTAL LEARNING; MULTI-OBJECT TRACKING; OFFLINE; ONLINE LEARNING; ONLINE VIDEO; RANDOM FOREST CLASSIFIER; RANDOM FORESTS; STREAMING DATA; TRAINING DATA SETS; TREE RECONSTRUCTION; VIDEO SEQUENCES;

DECISION TREES; E-LEARNING; PLANT EXTRACTS;

LEARNING ALGORITHMS;

EID: 80053096051     PISSN: 10009825     EISSN: None     Source Type: Journal    
DOI: 10.3724/SP.J.1001.2011.03827     Document Type: Article

References (27)

1. Schlimmer J C, Fisher D H. A case study of incremental concept induction. In: Proc. of the 5th National Conf. on Artificial Intelligence Philadelphia. Morgan Kaufmann Publishers, 1986. 496-501. https://www.aaai.org/Papers/AAAI/1986/AAAI86-083.pdf
2. Quinlan J R. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, 1993.
3. Cauwenberghs G, Poggio T. Incremental and decremental support vector machine learning. Neural Information Processing Systems, 2000. 409-415. http://www.cs.cmu.edu/Web/Groups/NIPS/00papers-pub- on-web/CauwenberghsPoggio.ps.gz
4. Crammer K, Singer Y. Ultraconservative online algorithms for multiclass problems. Journal of Machine Learning Research, 2003, 3: 951-991.
5. Xiao R, Wang J C, Sun Z X, Zhang F Y. An incremental SVM learning algorithm α-ISVM. Journal of Software, 2001, 12(12): 1818-1824 (in Chinese with English abstract). http://www.jos.org.cn/ch/reader/view_abstract. aspx?flag=&file_no=20011211&journal_id=jos
6. Ye N, Li X Y. A machine learning algorithm based on supervised clustering and classification. In: Proc. of the 6th Int'l Computer Science Conf. on AMT 2001. LNCS 2252, 2001. 327-334. http://www.springerlink.com/content/vlt36tngcauuaruq/
7. Oza N C, Russell S. Online bagging and boosting. In: Proc. of the 8th Int'l Workshop on Artificial Intelligence and Statistics. 2001. 105-112. http://www.cs.berkeley.edu/~oza/papers/aistats01.ps
8. Utgoff P E. Incremental induction of decision trees. Machine Learning, 1989, 4: 161-186. [doi:10.1023/A:1022699900025]
9. Utgoff P E, Berkman N C, Clouse J A. Decision tree induction based on efficient tree restructuring. Machine Learning, 1997, 29: 5-44.
10. Luo B, Zhou Z H, Chen Z Q, Chen S F. Induce: An incremental decision tree algorithm. Journal of Computer Research and Development, 1999, 36(5): 518-522 (in Chinese with English abstract).
11. Domingos P, Hulten G. Mining high-speed data streams. In: Proc. of the Int'l Conf. on Knowledge Discovery and Data Mining. 2000. 71-80. http://www.cs.washington.edu/homes/pedrod/papers/kdd00.pdf
12. Gama J, Rocha R, Medas P. Accurate decision trees for mining high speed data streams. In: Proc. of the ACM SIGKDD 2003. 2003. 523-528. http://magna.cs.ucla.edu/~hxwang/stream/gama-kdd03.pdf
13. Kirkby R. Improving hoeffding trees [Ph.D. Thesis]. Department of Computer Science, University of Waikato, 2007.
14. Wang T, Li Z J, Hu X H, Yan Y J, Chen H W. An incremental fuzzy decision tree classification method for data streams mining based on threaded binary search trees. Chinese Journal of Computers, 2007, 30(8): 1244-1250 (in Chinese with English abstract).
15. Abdulsalam H. Streaming random forests [Ph.D. Thesis]. Kingston: Queen's University, 2008.
16. del Campo-Ávila J, Ramos-Jiménez G, Gama J, Morales-Bueno R. Improving prediction accuracy of an incremental algorithm driven by error margins. Intelligent Data Analysis, 2008, 12(3): 305-318.
17. Friedman N, Goldszmidt M. Sequential update of Bayesian network structure. In: Proc. of the 13th Conf. on Uncertainty in Artificial Intelligence. 1997. 165-174. http://www.cs.huji.ac.il/~nir/Papers/FrG4.pdf
18. Okamato K, Ozawa S, Abe S. A fast incremental learning algorithm of RBF networks with long-term memory. In: Proc. of the Int'l Joint Conf. on Neural Networks. 2003. 102-107. http://www2.kobe-u.ac.jp/~ozawasei/pub/ijcnn03a.pdf
19. Witten I H, Frank E. Data mining: Practical machine learning tools and techniques. Morgan Kaufmann Publishers, 2005.
20. Tang F, Brennan S, Zhao Q, Tao H. Co-Tracking using semi-supervised support vector machines. IEEE Int'l Conf. on Computer Vision. 2007. 1-8. [doi:10.1109/ICCV.2007.4408954]
21. Zhu X J. Semi-Supervised learning literature survey. Madison: Department of Computer Sciences, University of Wisconsin, 2007.
22. Geurts P, Ernst D, Wehenkel L. Extremely randomized trees. Machine Learning, 2006, 63: 3-42. [doi:10.1007/S10994-006-6226-1]
23. Breiman L. Random forests, Machine Learning, 2001, 45: 5-32.
24. Breiman L, Friedman J H, Olshen R A, Stone C J. Classification and Regression Trees. Monterey: Wadsworth and Brooks, 1984.
25. Hong Y, Kwong S, Chang Y C, Ren Q S. Unsupervised data pruning for clustering of noisy data. Knowledge-Based Systems, 2008, 21: 612-616. [doi:10.1016/j.knosys.2008.03.052]
26. Dalal N, Triggs B. Histograms of oriented gradients for human detection. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition. 2005. 886-893. http://lear.inrialpes.fr/pubs/2005/DT05/cvpr2005_talk.pdf
27. Adam A, Rivlin E, Shimshoni I. Robust fragments-based tracking using the integral histogram. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition. 2006. 798-805