Classifier ensembles with a random linear oracle

IEEE Transactions on Knowledge and Data Engineering

Volumn 19, Issue 4, 2007, Pages 500-508

  Kuncheva, Ludmila I ^a   Rodríguez, Juan J ^b  

^a BANGOR UNIVERSITY   (United Kingdom)

^b UNIVERSITY OF BURGOS   (Spain)

Author keywords

Classifier ensembles; Fusion and selection; Multivariate (oblique) decision trees; Random hyperplane

Indexed keywords

CLASSIFIER ENSEMBLES; DATA COLLECTIONS; DATA SETS; ENSEMBLE MEMBERS; ENSEMBLE METHODS; ENSEMBLE MODELS; FUSION AND SELECTION; MEDICAL DATA SETS; MULTIVARIATE (OBLIQUE) DECISION TREES; NEW APPROACHES; RANDOM HYPERPLANE; RANDOM SUBSPACES;

DECISION TREES; LEARNING SYSTEMS;

CLASSIFIERS;

EID: 37249077843     PISSN: 10414347     EISSN: None     Source Type: Journal    
DOI: 10.1109/TKDE.2007.1016     Document Type: Article

References (43)

1. E. Alpaydin and M.I. Jordan, "Local Linear Perceptrons for Classification," IEEE Trans. Neural Networks, vol. 7, no. 3, pp. 788- 792, May 1996.
2. R. Avnimelech and N. Intrator, "Boosted Mixture of Experts: An Ensemble Learning Scheme," Neural Computation, vol. 11, pp. 475- 490, 1999.
3. C.L. Blake and C.J. Merz, "UCI Repository of Machine Learning Databases," http://www.ics.uci.edu/-mlearn/MLRepository. html, 1998.
4. L. Breiman, "Bagging Predictors," Technical Report 421, Dept. of Statistics, Univ. of California, Berkeley, 1994.
5. L. Breiman, "Random Forests," Machine Learning, vol. 45, pp. 5-32, 2001.
6. C.E. Brodley and P.E. Utgoff, "Multivariate Decision Trees," Machine Learning, vol. 19, no. 1, pp. 45-77, 1995.
7. E. Cantú-Paz and C. Kamath, "Inducing Oblique Decision Trees with Evolutionary Algorithms," IEEE Trans. Evolutionary Comput- ing, vol. 7, no. 1, pp. 54-68, Feb. 2003.
8. B.V. Dasarathy and B.V. Sheela, "A Composite Classifier System Design: Concepts and Methodology," Proc. IEEE, vol. 67, pp. 708- 713, 1978.
9. L. Didaci and G. Giacinto, "Dynamic Classifier Selection by Adaptive k-Nearest-Neighbourhood Rule," Proc. Fifth Int'l Work- shop Multiple Classifier Systems (MCS Ό4), pp. 174-183, 2004.
10. L. Didaci, G. Giacinto, F. Roli, and G.L. Marcialis, "A Study on the Performances of Dynamic Classifier Selection Based on Local Accuracy Estimation," Pattern Recognition, vol. 38, no. 11, pp. 2188- 2191, 2005.
11. J.L. Fleiss, Statistical Methods for Rates and Proportions. John Wiley & Sons, 1981.
12. Y. Freund and R.E. Schapire, "A Decision-Theoretic General- ization of On-Line Learning and an Application to Boosting," J. Computer and System Sciences, vol. 55, no. 1, pp. 119-139, 1997.
13. G. Giacinto and F. Roli, "An Approach to the Automatic Design of Multiple Classifier Systems," Pattern Recognition Letters, vol. 22, pp. 25-33, 2001.
14. D.G. Heath, S. Kasif, and S. Salzberg, "Induction of Oblique Decision Trees," Proc. Int'l Joint Conf. Artificial Intelligence (IJCAI), pp. 1002-1007, 1993.
15. T.K. Ho, "The Random Space Method for Constructing Decision Forests," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 8, pp. 832-844, Aug. 1998.
16. R.A. Jacobs, M.I. Jordan, S.J. Nowlan, and G.E. Hinton, "Adaptive Mixtures of Local Experts," Neural Computation, vol. 3, pp. 79-87, 1991.
17. M.S. Kamel and N.M. Wanas, "Data Dependence in Combining Classifiers," Proc. Fourth Int'l Workshop Multiple Classifier Systems (MCS Ό3), pp. 1-14, 2003.
18. L.I. Kuncheva, Combining Pattern Classifiers: Methods and Algo- rithms. John Wiley & Sons, 2004.
19. L.I. Kuncheva and C.J. Whitaker, "Measures of Diversity in Classifier Ensembles," Machine Learning, vol. 51, pp. 181-207, 2003.
20. L.I. Kuncheva, "Change-Glasses Approach in Pattern Recogni- tion," Pattern Recognition Letters, vol. 14, pp. 619-623, 1993.
21. L.I. Kuncheva, "Switching between Selection and Fusion in Combining Classifiers: An Experiment," IEEE Trans. Systems, Man, and Cybernetics, Part B, vol. 32, no. 2, pp. 146-156, Apr. 2002.
22. L.I. Kuncheva, "Diversity in Multiple Classifier Systems (Editor- ial)," Information Fusion, vol. 6, no. 1, pp. 3-4, 2005.
23. L.I. Kuncheva, C.J. Whitaker, C.A. Shipp, and R.P.W. Duin, "Is Independence Good for Combining Classifiers," Proc. 15th Int'l Conf. Pattern Recognition, vol. 2, pp. 169-171, 2000.
24. X.B. Li, J.R. Sweigar, J.T.C. Teng, J.M. Donohue, L.A. Thombs, and S.M. Wang, "Multivariate Decision Trees Using Linear Discrimi- nants and Tabu Search," IEEE Trans. Systems, Man, and Cybernetics, Part A, vol. 33, no. 2, pp. 194-205, Mar. 2003.
25. Y. Li, M. Dong, and R. Kothari, "Classifiability-Based Omnivariate Decision Trees," IEEE Trans. Neural Networks, vol. 16, no. 6, pp. 1547-1560, Nov. 2005.
26. D.D. Margineantu and T.G. Dietterich, "Pruning Adaptive Boosting," Proc. 14th Int'l Conf. Machine Learning, pp. 378-387, 1997.
27. P. Melville and R.J. Mooney, "Creating Diversity in Ensembles Using Artificial Data," Information Fusion, vol. 6, no. 1, pp. 99-111, 2005.
28. P. Moerland and E. Mayoraz, "Dynaboost: Combining Boosted Hypotheses in a Dynamic Way," Technical Report IDIAP-RR99- 09, Dalle Molle Inst. for Perceptual Artificial Intelligence (IDIAP), 1999.
29. K.V.S. Murthy, "On Growing Better Decision Trees from Data," PhD dissertation, Johns Hopkins Univ., 1995.
30. S.K. Murthy, S. Kasif, and S. Salzberg, "A System for Induction of Oblique Decision Trees," J. Artificial Intelligence Research, vol. 2, pp. 1-32, 1994.
31. L.A. Rastrigin and R.H. Erenstein, Method of Collective Recognition, 1981 (in Russian).
32. J.J. Rodriguez, L.I. Kuncheva, and C.J. Alonso, "Rotation Forest: A New Classifier Ensemble Method," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 10, pp. 1619-1630, Oct. 2006.
33. J. Demsar, "Statistical Comparison of Classifiers over Multiple Data Sets," J. Machine Learning Research, vol. 7, pp. 1-30, 2006.
34. H.W. Shin and S.Y. Sohn, "Selected Tree Classifier Combination Based on Both Accuracy and Error Diversity," Pattern Recognition, vol. 38, no. 2, pp. 191-197, 2005.
35. S. Singh and M. Singh, "A Dynamic Classifier Selection and Combination Approach to Image Region Labelling," Signal Processing-Image Comm., vol. 20, no. 3, pp. 219-231, 2005.
36. F. Smieja, "The Pandemonium System of Reflective Agents," IEEE Trans. Neural Networks, vol. 7, no. 1, pp. 97-106, Jan. 1996.
37. P.C. Smits, "Multiple Classifier Systems for Supervised Remote Sensing Image Classification Based on Dynamic Classifier Selec- tion," IEEE Trans. Geoscience and Remote Sensing, vol. 40, no. 4, pp. 801-813, Apr. 2002.
38. P.J. Tan and D.L. Dowe, "MML Inference of Oblique Decision Trees," Proc. 17th Australian Joint Conf. Artificial Intelligence (AI Ό4), pp. 1082-1088, 2004.
39. A. Verikas, A. Lipnickas, K. Malmqvist, M. Bacauskiene, and A. Gelzinis, "Soft Combination of Neural Classifiers: A Comparative Study," Pattern Recognition Letters, vol. 20, pp. 429-444, 1999.
40. G.I. Webb, "MultiBoosting: A Technique for Combining Boosting and Wagging," Machine Learning, vol. 40, no. 2, pp. 159-196, 2000.
41. I.H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, second ed. Morgan Kaufmann, 2005.
42. K. Woods, W.P. Kegelmeyer, and K. Bowyer, "Combination of Multiple Classifiers Using Local Accuracy Estimates," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, pp. 405-410, 1997.
43. O.T. Yildiz and E. Alpaydin, "Omnivariate Decision Trees," IEEE Trans. Neural Networks, vol. 12, no. 6, pp. 1539-1546, Nov. 2001.