Pruning of random forest classifiers: A survey and future directions

Proceedings - 2012 International Conference on Data Science and Engineering, ICDSE 2012

Volumn , Issue , 2012, Pages 64-68

  Kulkarni, Vrushali Y ^a   Sinha, Pradeep K ^b  

^a Department of Instrumentation & Control Engineering   (India)

^b CENTRE FOR DEVELOPMENT OF ADVANCED COMPUTING   (India)

Author keywords

Classification; Data Mining; Ensemble; Machine Learning; Pruning; Random Forest

Indexed keywords

BASE CLASSIFIERS; DYNAMIC PRUNING; EFFECTIVE LEARNING; ENSEMBLE; MAJORITY VOTING; OPTIMAL SUBSETS; PRUNING; RANDOM FOREST CLASSIFIER; RANDOM FORESTS; STATIC APPROACH; SUPERVISED MACHINE LEARNING;

CLASSIFICATION (OF INFORMATION); DATA MINING; FORESTRY; LEARNING SYSTEMS; SURVEYS;

DECISION TREES;

CLASSIFICATION; DATA PROCESSING; DECISION MAKING; FORESTRY; PRUNING; SURVEYS; TREES;

EID: 84867380027     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDSE.2012.6282329     Document Type: Conference Paper

References (16)

1. Leo Brieman, Random Forests, Machine Learning, 45, 5-32, (2001)
2. Leo Breiman, Bagging Predictors , Technical report No 421, (September 1994)
3. David Opitz, Richard Maclin, Popular Ensemble Methods: An Empirical Study, Journal of Artificial Intelligence 11, 169-198, (1999)
4. Heping Zhang, Minghui Wang, Search for the smallest Random Forest, Statistics and Its Interface Volume 2, pp 381-388, (2009)
5. Simon Bernard, Laurent Heutte, and Sebastien Adam, On the Selection of Decision Trees in Random Forest, Proceedings of International Joint Cobference on Neural Networks, Atlanta, Georgia, USA, June 14-19, pp 302-307, (2009)
6. P. Latinne, O. Debeir, C. Decastecker, Limiting the number of trees in Random Forest, MCS, UK (2001)
7. E Tripoli, D Fotiadis, G Manis, Dynamic Construction of Random Forests: Evaluation using Biomedical Engineering Problems, IEEE 2010
8. Xuanfu Wu, Zhengxin Chen, Toward Dynamic Ensemble: The BAGA Approach, Proceedings of the ACS/ IEEE International Conference on Computer Systems and Applications,(2005)
9. Vahida Attar and Pradeep Sinha, Research Directions for Building Future Decision Tree Classifiers for Large Datasets, IJCITAE, Vol No 2, 53-63 @ Serials Publication (December 2008)
10. S Bernard, L Heutte, and S Adam, Towards a Better Understanding of Random Forests Through the Study of Strength and Correlation, ICIC Proceedings of the Intelligent Computing 5th International Conference on Emerging Intelligent Computing Technology and Applications, (2009)
11. F Roli, G Giacinto, G Vernazza, Methods for Designing Multiple Classifier Systems, Second International Workshop on Multiple Classifier Systems, Springer-Verlag (2001)
12. G Giacinto, F Roli, G Fumera, Design of Effective Multiple Classifier Systems by Clustering of Classifiers, 15th International Conference on Pattern Recognition, Vol 2, 160-163, (2000)
13. R Kohavi and G John, Wrappers for Feature Subset selection, Artificial Intelligence 97, 273-324, (1997)
14. Opitz D, Shavlik J, Generating Accurate and Diverse Members of a Neural-Network Ensemble, Advances in Neural Information Processing Systems Vol 8, MIT Press , (1996)
15. L Kuncheva, C Whitaker, Measures of Diversity in Classifier Ensembles and their relationship with the Ensemble Accuracy, Machine Learning, 51, 181-207, 2003
16. L Kuncheva, A Bound on Kappa-Error Diagrams for Analysis of Classifier Ensembles, IEEE Transaction on Knowledge and Data Engineering, Nov 2011