Ensembles of grafted trees

Frontiers in Artificial Intelligence and Applications

Volumn 141, Issue , 2006, Pages 803-804

  Rodríguez, Juan J ^a   Maudes, Jesús M ^a  

^a UNIVERSITY OF BURGOS   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); FORESTRY;

ENSEMBLE METHODS; RANDOM FORESTS; TRAINING DATA;

DECISION TREES;

EID: 84886060508     PISSN: 09226389     EISSN: 18798314     Source Type: Book Series    
DOI: None     Document Type: Article

References (13)

1. C. L. Blake and C. J. Merz. UCI repository of machine learning databases, 1998. http://www.ics.uci.edu/-mlearn/MLRepository.html.
2. L. Breiman, 'Bagging predictors', Machine Learning, 24(2), 123-140, (1996).
3. L. Breiman, 'Random forests', Machine Learning, 45(1), 5-32, (2001).
4. J. Demšar, 'Statistical comparisons of classifiers over multiple data sets', Journal of Machine Learning Research, 7, 1-30, (2006).
5. T. G. Dietterich, 'An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization', Machine Learning, 40(2), 139-158, (2000).
6. Y. Freund and R. E. Schapire, 'A decision-theoretic generalization of on-line learning and an application to boosting', Journal of Computer and System Sciences, 55(1), 119-139, (August 1997).
7. T. K. Ho, 'The random subspace method for constructing decision forests', IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8), 832-844, (1998).
8. L. I. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms, Wiley-Interscience, 2004.
9. J. Li and H. Liu, 'Ensembles of cascading trees', in 3rd IEEE International Conference on Data Mining (ICDM'03), pp. 585-588, (2003).
10. C. Nadeau and Y. Bengio, 'Inference for the generalization error', Machine Learning, 52(239-281), (2003).
11. R. E. Schapire, 'The boosting approach to machine learning: An overview', in MSRI Workshop on Nonlinear Estimation and Classification, (2002).
12. G. I. Webb, 'Decision tree grafting from the all tests but one partition', in 16th International Joint Conference on Artificial Intelligence (IJCAI 99). Morgan Kaufmann, (1999).
13. I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2nd edn., 2005.