Leo Breimans Random Forests (RF) is a recent
development in tree based classifiers and quickly proven to be one of
the most important algorithms in the machine learning literature. It
has shown robust and improved results of classifications on standard
data sets. Ensemble learning algorithms such as AdaBoost and
Bagging have been in active research and shown improvements in
classification results for several benchmarking data sets with mainly
decision trees as their base classifiers. In this paper we experiment to
apply these Meta learning techniques to the random forests. We
experiment the working of the ensembles of random forests on the
standard data sets available in UCI data sets. We compare the
original random forest algorithm with their ensemble counterparts
and discuss the results.
[1] Breiman, L.: Random Forests Technical Report, University of
California, 2001.
[2] http://www.stat.berkeley.edu/users/breiman/RandomForests/cc_home.ht
m#intro
[3] Breiman, L.: Looking Inside the Black Box, Wald Lecture II, Department
of Statistics, California University, 2002.
[4] Sikonja. M, Improving Random Forests. In J.-F. Boulicaut et al.(Eds):
ECML 2004, LNAI 3210, Springer, Berlin, 2004, pp. 359-370.
[5] Leo Breiman, Jerome H. Friedman, Richard A. Olshen, and Charles J.
Stone. Classification and regression trees. Wadsworth Inc., Belmont,
California, 1984.
[6] J. Ross Quinlan. C4.5: Programs for Machine Learning. Morgan
Kaufmann, San Francisco, 1993.
[7] Igor Kononenko. Estimating attributes: analysis and extensions of
Relief. In Luc De Raedt and Francesco Bergadano, editors, Machine
Learning: ECML-94, pages 171-182. Springer Verlag, Berlin, 1994.
[8] Igor Kononenko. On biases in estimating multi-valued attributes. In
Proceedings of the International Joint Conference on Artificial
Intelligence (IJCAI-95), pages 1034-1040. Morgan Kaufmann, 1995.
[9] Thomas G. Dietterich, Michael Kerns, and Yishay Mansour. Applying
the weak learning framework to understand and improve C4.5. In
Lorenza Saitta, editor, Machine Learning: Proceedings of the Thirteenth
International Conference (ICML-96), pages 96-103. Morgan Kaufmann,
San Francisco, 1996.
[10] Leo Breiman, Jerome H. Friedman, Richard A. Olshen, and Charles J.
Stone. Classification and regression trees. Wadsworth Inc., Belmont,
California, 1984.
[11] http://www.ics.uci.edu/~mlearn/MLRepository.html
[12] J.R. Quinlan, Bagging, Boosting, and C4.5, In Proceedings, Fourteenth
National Conference on Artificial Intelligence, 1996.
[13] Robert E. Schapire, Yoav Freund, Peter Bartlett, and Wee Sun Lee.
Boosting the margin: a new explanation for the effectiveness of voting
methods. In Douglas H. Fisher, editor, Machine Learning: Proceedings
of the Fourteenth International Conference (ICML-97), pages 322-330.
Morgan Kaufmann, 1997.
[14] Breiman, L., Bagging Predictors, Machine Learning (1996) 24:123-
140.
[15] Carney, J., Cunningham, P.: The NeuralBAG algorithm: optimizing
generalization performance in Bagged Neural Networks. In: Verleysen,
M. (eds.): Proceedings of the 7th European Symposium on Artificial
Neural Networks (1999), pp. 3540.
[16] Freund, Y., Schapire, RE.: Experiments with a new boosting algorithm.
In Proceedings 13th International Conference on Machine Learning
(1996) 148-156.
[17] Skurichina, M., Duin, R.P.W.: Bagging, Boosting and the Random
Subspace Method for Linear Classifiers, Vol. 5, no. 2, Pattern Analysis
and Applications (2002) 121-135.
[1] Breiman, L.: Random Forests Technical Report, University of
California, 2001.
[2] http://www.stat.berkeley.edu/users/breiman/RandomForests/cc_home.ht
m#intro
[3] Breiman, L.: Looking Inside the Black Box, Wald Lecture II, Department
of Statistics, California University, 2002.
[4] Sikonja. M, Improving Random Forests. In J.-F. Boulicaut et al.(Eds):
ECML 2004, LNAI 3210, Springer, Berlin, 2004, pp. 359-370.
[5] Leo Breiman, Jerome H. Friedman, Richard A. Olshen, and Charles J.
Stone. Classification and regression trees. Wadsworth Inc., Belmont,
California, 1984.
[6] J. Ross Quinlan. C4.5: Programs for Machine Learning. Morgan
Kaufmann, San Francisco, 1993.
[7] Igor Kononenko. Estimating attributes: analysis and extensions of
Relief. In Luc De Raedt and Francesco Bergadano, editors, Machine
Learning: ECML-94, pages 171-182. Springer Verlag, Berlin, 1994.
[8] Igor Kononenko. On biases in estimating multi-valued attributes. In
Proceedings of the International Joint Conference on Artificial
Intelligence (IJCAI-95), pages 1034-1040. Morgan Kaufmann, 1995.
[9] Thomas G. Dietterich, Michael Kerns, and Yishay Mansour. Applying
the weak learning framework to understand and improve C4.5. In
Lorenza Saitta, editor, Machine Learning: Proceedings of the Thirteenth
International Conference (ICML-96), pages 96-103. Morgan Kaufmann,
San Francisco, 1996.
[10] Leo Breiman, Jerome H. Friedman, Richard A. Olshen, and Charles J.
Stone. Classification and regression trees. Wadsworth Inc., Belmont,
California, 1984.
[11] http://www.ics.uci.edu/~mlearn/MLRepository.html
[12] J.R. Quinlan, Bagging, Boosting, and C4.5, In Proceedings, Fourteenth
National Conference on Artificial Intelligence, 1996.
[13] Robert E. Schapire, Yoav Freund, Peter Bartlett, and Wee Sun Lee.
Boosting the margin: a new explanation for the effectiveness of voting
methods. In Douglas H. Fisher, editor, Machine Learning: Proceedings
of the Fourteenth International Conference (ICML-97), pages 322-330.
Morgan Kaufmann, 1997.
[14] Breiman, L., Bagging Predictors, Machine Learning (1996) 24:123-
140.
[15] Carney, J., Cunningham, P.: The NeuralBAG algorithm: optimizing
generalization performance in Bagged Neural Networks. In: Verleysen,
M. (eds.): Proceedings of the 7th European Symposium on Artificial
Neural Networks (1999), pp. 3540.
[16] Freund, Y., Schapire, RE.: Experiments with a new boosting algorithm.
In Proceedings 13th International Conference on Machine Learning
(1996) 148-156.
[17] Skurichina, M., Duin, R.P.W.: Bagging, Boosting and the Random
Subspace Method for Linear Classifiers, Vol. 5, no. 2, Pattern Analysis
and Applications (2002) 121-135.
@article{"International Journal of Information, Control and Computer Sciences:52912", author = "Praveen Boinee and Alessandro De Angelis and Gian Luca Foresti", title = "Meta Random Forests", abstract = "Leo Breimans Random Forests (RF) is a recent
development in tree based classifiers and quickly proven to be one of
the most important algorithms in the machine learning literature. It
has shown robust and improved results of classifications on standard
data sets. Ensemble learning algorithms such as AdaBoost and
Bagging have been in active research and shown improvements in
classification results for several benchmarking data sets with mainly
decision trees as their base classifiers. In this paper we experiment to
apply these Meta learning techniques to the random forests. We
experiment the working of the ensembles of random forests on the
standard data sets available in UCI data sets. We compare the
original random forest algorithm with their ensemble counterparts
and discuss the results.", keywords = "Random Forests [RF], ensembles, UCI.", volume = "2", number = "6", pages = "1894-10", }