Maximizing tree diversity by building complete-random decision trees

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

Volumn 3518 LNAI, Issue , 2005, Pages 605-610

  Liu, Fei Tony ^a   Ting, Kai Ming ^a   Fan, Wei ^b  

^a UNIVERSITY OF BALLARAT   (Australia)

^b IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MATHEMATICAL TECHNIQUES; OPTIMIZATION; RANDOM PROCESSES; SAMPLING; TREES (MATHEMATICS);

DECISION TREES; RANDOMIZATION METHODS; TEST SELECTION; TREE DIVERSITY;

DECISION THEORY;

EID: 26944434312     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11430919_70     Document Type: Conference Paper

References (10)

1. Yali Amit and Donald Geman. Shape quantization and recognition with randomized trees. Neural Computation, 9(7):1545-1588, 1997.
2. C.L. Blake and C.J. Merz. Uci repository of machine learning databases, 1998.
3. Leo Breiman. Bagging predictors. Machine Learning, 24(2):123-140, 1996.
4. Leo Breiman. Randomizing outputs to increase prediction accuracy. Machine Learning, 40(3):229-242, 2000.
5. Leo Breiman. Random forests. Machine Learning, 45(1):5-32, 2001.
6. Thomas G. Dietterich. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning, 40(2): 139-157, 2000.
7. Pedro Domingos. Bayesian averaging of classifiers and the overfitting problem. In Proc. 17th International Conf. on Machine Learning, pages 223-230. Morgan Kaufmann, San Francisco, CA, 2000.
8. Wei Fan, Haixun Wang, Philip S. Yu, and Sheng Ma. Is random model better? on its accuracy and efficiency. Third IEEE International Conference on Data Mining, pages 51-58, 2003.
9. Tin Kam Ho. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8):832-844, 1998.
10. J. R. Quinlan. C4.5: programs for machine learning. Morgan Kaufmann Publishers, San Mateo, Calif., 1993.