Learning with ensembles of randomized trees: New insights

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6323 LNAI, Issue PART 3, 2010, Pages 67-82

  Pisetta, Vincent ^a   Jouve, Pierre Emmanuel ^b   Zighed, Djamel A ^c  

^a Rithme   (France)

^b Fenics   (France)

^c ERIC Laboratory   (France)

Author keywords

Ensemble Learning; Infinite Ensembles; Kernel Target Alignment; Randomized Trees Ensembles

Indexed keywords

BASIS FUNCTIONS; ENSEMBLE CLASSIFIERS; ENSEMBLE LEARNING; GENERALIZATION ERROR; INFINITE ENSEMBLES; KERNEL TARGET ALIGNMENT; LEARNING ABILITIES; LINEAR SEPARATORS; MACHINE-LEARNING; MAJORITY VOTING; POST PROCESS; POST PROCESSING; RANDOM FORESTS; RANDOMIZED TREES ENSEMBLES; VOTING SCHEMES;

ALIGNMENT; DECISION TREES; SUPPORT VECTOR MACHINES;

LEARNING ALGORITHMS;

EID: 77958023968     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15939-8_5     Document Type: Conference Paper

References (31)

1. Biau, G., Devroye, L., Lugosi, G.: Consistency of random forests and other aver-aging classifiers. Journal of Machine Learning Research 9, 2015-2033 (2008)
2. Breiman, L.: Bagging predictors. Machine Learning 24, 123-140 (1996)
3. Breiman, L.: Bias, variance and arcing classifiers (1996), http://www.sasenterpriseminer.com/documents/arcing.pdf
4. Breiman, L.: Some infinity theory for predictor ensembles (2000), http://www.stat.berkeley.edu
5. Breiman, L.: Random forests. Machine Learning 45, 5-32 (2001)
6. Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees. Wadsworth and Brooks (1984)
7. Buntine, W., Niblett, T.: A further comparison of splitting rules for decision tree induction. Machine Learning 8, 75-85 (1992)
8. Chang, C.-C., Lin, C.-J.: Libsvm: A library for support vector machines (2001), Software available at, http://www.csie.ntu.edu.tw/cjlin/libsvm
9. Cristianini, N., Kandola, J., Elisseeff, A., Shawe-Taylor, J.: On kernel-target align-ment. In: Holmes, D., Jain, L. (eds.) Innovations in Machine Learning: Theory and Application, pp. 205-255 (2006)
10. Cutler, A., Zhao, G.: Pert-perfect random tree ensembles. Computer Science and Statistics (2001)
11. Demiriz, A., Bennett, K., Shawe-Taylor, J.: Linear programming boosting via col-umn generation. Machine Learning 46, 225-254 (2002)
12. Dietterich, T.: An experimental comparison of three methods for constructing en-sembles of decision trees: Bagging, boosting, and randomization. Machine Learn-ing 40, 139-157 (2000)
13. Freund, Y., Schapire, R.: Experiments with a new boosting algorithm. In: ICML, pp. 148-156 (1996)
14. Freund, Y., Schapire, R.: A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence 14, 771-780 (1999)
15. Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. The Annals of Statistics 38, 95-118 (2000)
16. Friedman, J., Popescu, B.: Predictive learning via rule ensembles. The Annals of Applied Statistics 2, 916-954 (2008)
17. Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Machine Learn-ing 63, 3-42 (2006)
18. Guyon, I., Gunn, S., Ben-Hur, A., Dror, G.: Result analysis of the nips 2003 feature selection challenge. In: Saul, L.K., Weiss, Y., Bottou, L. (eds.) Advances in Neu-ral Information Processing Systems, vol. 17, pp. 545-552. MIT Press, Cambridge (2005)
19. Hastie, T., Tibshirani, R., Friedman, J.: The elements ofstatistical learning: Data Mining, Inference and Prediction. Springer, Heidelberg (2009)
20. Hsu, C.-W., Chang, C.-C., Lin, C.-J.: A practical guide to support vector classifi-cation (2003), http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide. pdf
21. Kleinberg, E.: On the algorithmic implementation of stochastic discrimination. IEEE Trans. Pattern Anal. Mach. Intell. 22, 473-490 (2000)
22. Kuncheva, L., Whitaker, C.: Measures ofdiversity in classifier ensembles and their relationship with the ensemble accuracy. Machine Learning 51, 181-207 (2003)
23. Lin, H.-T., Li, L.: Support vector machinery for infinite ensemble learning. Journal of Machine Learning Research 9, 941-973 (2008)
24. Liu, F.-T., Ting, K.-M., Yu, Y., Zhou, Z.-H.: Spectrum ofVariable-Random Trees. Journal of Artifical Intelligence Research 32, 355-384 (2008)
25. Quinlan, J.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Fran-cisco (1993)
26. Ratsch, G., Onoda, T., Muller, K.-R.: Soft margins for adaboost. Machine Learn-ing 42, 287-320 (2001)
27. Rosset, S., Zhu, J., Hastie, T.: Boosting as a regularized path to a maximum margin classifier. Journal ofMachine Learning Research 5, 941-973 (2004)
28. Scholkopf, B., Smola, A.: Learning with Kernels. MIT Press, Cambridge (2001)
29. Utgoff, P., Clouse, J.: A kolmogorov-smirnov metric for decision tree induction (1996), http://www.ics.uci.edu/~mlearn/MLRepository.html
30. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)
31. Wolpert, D.: Stacked Generalization. Neural Networks 5, 241-259 (1992)