Decision forests with oblique decision trees

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

Volumn 4293 LNAI, Issue , 2006, Pages 593-603

  Tan, Peter J ^a   Dowe, David L ^a  

^a MONASH UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

DATA STRUCTURES; LEARNING ALGORITHMS; LEARNING SYSTEMS; PROBABILISTIC LOGICS; TREES (MATHEMATICS);

DECISION TREES; MINIMUM MESSAGE LENGTH (MML); PROBABILISTIC PREDICTIONS;

DECISION TABLES;

EID: 33845943268     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11925231_56     Document Type: Conference Paper

References (33)

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. Arcing classifiers. The Annals of Statistics, 26(3):801-824, Jun. 1998.
3. L. Breiman. Randomizing outputs to increase prediction accuracy. Machine Learing, 40:229-242, 2000.
4. Leo Breiman. Bagging predictors. Machine Learning, 24(2):123-140, 1996.
5. Leo Breiman. Random forests. Machine Learning, 45(1):5, 2001.
6. Leo Breiman, Jerome H. Friedman, Richard A. Olshen, and Charles J. Stone. Classification And Regression Trees. Wadsworth & Brooks, 1984.
7. Joshua W. Comley and David L. Dowe. Generalised Bayesian networks and asymmetric languages. In Proc. Hawaii International Conference on Statistics and Related Fields, 5-8 June 2003.
8. Joshua W. Comley and David L. Dowe. Chapter 11, Minimum Message Length and generalized Bayesian networks with asymmetric languages. In P. Grünwald, M. A. Pitt, and I. J. Myung, editors, Advances in Minimum Description Length: Theory and Applications, pages 265-294. M.I.T. Press, Apr 2005. Final camera-ready copy submitted Oct. 2003.
9. Janez Demšar. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7:1-30, January 2006.
10. Thomas G. Dietterich. Machine-learning research: Four current directions. The AI Magazine, 18(4):97-136, 1998.
11. 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.
12. D. L. Dowe, G.E. Farr, A.J. Hurst, and K.L. Lentin. Information-theoretic football tipping. In N. de Mestre, editor, Third Australian Conference on Mathematics and Computers in Sport, pages 233-241. Bond University, Qld, Australia, 1996. http://www.csse.monash.edu.au/~footy.
13. D. L. Dowe, S. Gardner, and G. R. Oppy. Bayes not Bust! Why simplicity is no problem for Bayesians. British Journal for the Philosophy of Science, forthcoming.
14. D. L. Dowe and N. Krusel. A decision tree model of bushfire activity. In (Technical report 93/190) Dept. Comp. Sci., Monash Uni., Clayton, Australia, 1993.
15. D. L. Dowe and C. S. Wallace. Kolmogorov complexity, minimum message length and inverse learning. In 14th Australian Statistical Conference (ASC-14), page 144, Gold Coast, Qld, Australia, 6-10 July 1998.
16. Cesar Ferri, Peter Flach, and Jose Hernandez-Orallo. Delegating classifiers. In Proc. 21st International Conference on Machine Learning, pages 106-110, Banff, Canada, 2004.
17. Yoav Freund and Robert E. Schapire. Experiments with a new boosting algorithm. In International Conference on Machine Learning (ICML), pages 148-156, 1996.
18. Tin Kam Ho. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8):832-844, August 1998.
19. Manish Mehta, Jorma Rissanen, and Rakesh Agrawal. MDL-based Decision Tree Pruning, In The First International Conference on Knowledge Discovery & Data Mining, pages 216-221. AAAI Press, 1995.
20. Prem Melville and Raymond J. Mooney. Creating diversity in ensembles using artificial data. Journal of Information Fusion (Special Issue on Diversity in Multiple Classifier Systems), 6(1):99-111, 2004.
21. S. L. Needham and D. L. Dowe. Message length as an effective Ockham's razor in decision tree induction. In Proc. 8th International Workshop on Artificial Intelligence and Statistics, pages 253-260, Key West, Florida, U.S.A., Jan. 2001.
22. J. J. Oliver and C. S. Wallace. Inferring Decision Graphs. In Workshop 8 International Joint Conference on AI (IJCAI), Sydney, Australia, August 1991.
23. Jonathan J. Oliver and David J. Hand. On pruning and averaging decision trees. In A. Prieditis and S. Russell, editors, Machine Learning: Proceedings of the Twelfth International Conference, pages 430-437. Morgan Kaufmann, 1995.
24. J.R. Quinlan. C4.5 : Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA, U.S.A., 1992. The latest version of C5 is available from http://www.rulequest.com.
25. J.J. Rissanen. Modeling by shortest data description. Autornatica, 14:465-471, 1978.
26. P. J. Tan and D. L. Dowe. MML inference of decision graphs with multi-way joins. In Lecture Notes in Artificial Intelligence (LNAI) 2557 (Springer), Proc. 15th Australian Joint Conf. on AI, pages 131-142, Canberra, Australia, 2-6 Dec. 2002.
27. P. J. Tan and D. L. Dowe. MML inference of decision graphs with multi-way joins and dynamic attributes. In Lecture Notes in Artificial Intelligence (LNAI) 2903 (Springer), Proc. 16th Australian Joint Conf. on AI, pages 269-281, Perth, Australia, Dec. 2003.
28. P. J. Tan and D. L. Dowe. MML inference of oblique decision trees. In Lecture Notes in Artificial Intelligence (LNAI) 3339 (Springer), Proc. 17th Australian Joint Conf. on AI, pages 1082-1088, Cairns, Australia, Dec. 2004.
29. C. S. Wallace. Statistical and Inductive Inference by Minimum Message Length. Springer, 2005.
30. C. S. Wallace and D. M. Boulton. An Information Measure for Classification. Computer Journal, 11:185-194, 1968.
31. C. S. Wallace and D. L. Dowe. Minimum Message Length and Kolmogorov Complexity. Computer Journal, 42(4):270-283, 1999.
32. C. S. Wallace and P. R. Freeman. Estimation and Inference by Compact Coding. Journal of the Royal Statistical Society. Series B, 49(3):240-265, 1987.
33. Ian H. Witten and Eibe Frank. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, San Francisco, 2nd edition, 2005.