Boosting random subspace method

Neural Networks

Volumn 21, Issue 9, 2008, Pages 1344-1362

  García Pedrajas, Nicolás ^a   Ortiz Boyer, Domingo ^a  

^a UNIVERSITY OF CÓRDOBA   (Spain)

Author keywords

Boosting; Classification; Ensemble of classifiers; Random subspace method

Indexed keywords

CLASSIFIERS; CONTENT BASED RETRIEVAL; FACE RECOGNITION; RANDOM PROCESSES;

ADABOOST; BOOSTING; BOOSTING ALGORITHMS; BOOSTING APPROACHES; BOOSTING METHODS; CLASSIFICATION; CLASSIFICATION ERRORS; ENSEMBLE OF CLASSIFIERS; IMPROVED PERFORMANCES; K-NEAREST NEIGHBOR CLASSIFIERS; NEW APPROACHES; RANDOM SELECTIONS; RANDOM SUBSPACE METHOD; RANDOM SUBSPACE METHODS; RANDOM SUBSPACES; SEARCH SUBSPACES; SUB-SPACE; TRAINING INSTANCES; UCI MACHINE LEARNING REPOSITORIES;

LEARNING SYSTEMS;

ALGORITHM; ANALYTICAL ERROR; ARTICLE; CALCULATION; MACHINE LEARNING; MATHEMATICAL ANALYSIS; PRIORITY JOURNAL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CLASSIFICATION; GENETICS; MODELS, STATISTICAL; MUTATION; NEURAL NETWORKS (COMPUTER);

EID: 54449084620     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neunet.2007.12.046     Document Type: Article

References (45)

1. Bauer E., and Kohavi R. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning 36 1-2 (1999) 105-142
2. Breiman L. Stacked regressions. Machine Learning 24 1 (1996) 49-64
3. Breiman L. Bagging predictors. Machine Learning 24 2 (1996) 123-140
4. Breiman, L. (1996c) Bias, variance, and arcing classifiers, Tech. Rep. 460, Berkeley, CA: Department of Statistics, University of California
5. Breiman L. Random forests. Machine Learning 45 (2001) 5-32
6. Chang, C. -C., & Lin, C. -J. (2001) LIBSVM: Library for support vector machines. software available at: http://www.csie.ntu.edu.tw/~cjlin/libsvm
7. Chen K., Wang L., and Chi H. Methods of combining multiple classifiers with different features and their applications to text-independent speaker identification. Journal of Pattern Recognition and Artificial Intelligence 11 3 (1997) 417-445
8. Cherkauer, K. (1996) Human expert-level performance on a scientific image analysis task by a system using combined artificial neural networks. In Working notes of the aaai workshop on integrating multiple learned models (pp. 15-21)
9. Cristianini N., and Shawe-Taylor J. An introduction to support vector machines (2000), Cambridge University Press
10. Demšar J. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7 (2006) 1-30
11. Dietterich T.G. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation 10 7 (1998) 1895-1923
12. Dietterich T.G. Ensemble methods in machine learning. Lecture Notes in Computer Science 1857 (2000) 1-15
13. Dietterich T.G. Ensemble methods in machine learning. In: Kittler J., and Roli F. (Eds). Proceedings of the first international workshop on multiple classifier systems (2000), Springer-Verlag 1-15
14. Dietterich T.G. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning 40 (2000) 139-157
15. Dzeroski S., and Zenko B. Is combining classifiers with stacking better than selecting the best one?. Machine Learning 54 (2004) 255-273
16. Eshelman L.J. The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination (1990), Morgan Kauffman, San Mateo, CA
17. Fern A., and Givan R. Online ensemble learning: An empirical study. Machine Learning 53 (2003) 71-109
18. Freund, Y., & Schapire, R. (1996) Experiments with a new boosting algorithm, In Proc. of the thirteenth international conference on machine learning (pp. 148-156)
19. Friedman M. A comparison of alternative tests of significance for the problem of m rankings. Annals of Mathematical Statistics 11 (1940) 86-92
20. García-Pedrajas, N. (2007) Supervised projection approach for boosting classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence (submitted for publication)
21. García-Pedrajas N., García-Osorio C., and Fyfe C. Nonlinear boosting projections for ensemble construction. Journal of Machine Learning Research 8 (2007) 1-33
22. García-Pedrajas N., Hervás-Martínez C., and Ortiz-Boyer D. Cooperative coevolution of artificial neural network ensembles for pattern classification. IEEE Transactions on Evolutionary Computation 9 3 (2005) 271-302
23. Hall, L., Bowyer, K., Banfield, R., Bhadoria, D., Kegelmeyer, W., & Eschrich, S. (2003) Comparing pure parallel ensemble creation techniques against bagging. In Third IEEE international conference on data mining (pp. 533-536)
24. Hettich, S., Blake, C., & Merz, C. (1998) UCI repository of machine learning databases. http://www.ics.uci.edu/~mlearn/MLRepository.html
25. Ho T.K. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20 8 (1998) 832-844
26. Iman R.L., and Davenport J.M. Approximations of the critical regions of the friedman statistic. Communications in Statistics 6 (1980) 571-595
27. Keerthi S.S., and Lin C.-J. Asymptotic behaviors of support vector machines with a gaussian kernel. Neural Computation 15 (2003) 1667-1689
28. Kleinberg E. On the algorithmic implementation of stochastic discrimination. IEEE Transactions on Pattern Analysis and Machine Intelligence 22 5 (2000) 473-490
29. Kohavi R., and Kunz C. Option decision trees with majority voting. Proceedings of the fourteenth international conference on machine learning (1997), Morgan Kaufman, San Francisco, CA, USA 161-169
30. Kuncheva L.I. Combining classifiers: Soft computing solutions. In: Pal S.K., and Pal A. (Eds). Pattern recognition: From classical to modern approaches (2001), World Scientific 427-451
31. Louis S.J., and Li G. Combining robot control strategies using genetic algorithms with memory. Lecture Notes in Computer Science, Evolutionary Programming VI Vol. 1213 (1997) 431-442
32. Merz C.J. Using correspondence analysis to combine classifiers. Machine Learning 36 1 (1999) 33-58
33. Munro, R., Ler, D., & Patrick, J. (2003) Meta-learning orthographic and contextual models for language independent named entity recognition. In Proceedings of the seventh conference on natural language learning (pp. 192-195)
34. Ortiz-Boyer D., Hervás-Martínez C., and García-Pedrajas N. Cixl2: A crossover operator for evolutionary algorithms based on population features. Journal of Artificial Intelligence Research 24 (2005) 33-80
35. Quinlan J.R. C4.5: Programs for machine learning (1993), Morgan Kaufmann, San Mateo
36. Quinlan J.R. Bagging, boosting, and c4.5. Proceedings if the thirteenth national conference on artificial intelligence (1996), AAAI Press and the MIT Press 725-730
37. Rumelhart D., Hinton G., and Williams R.J. Learning internal representations by error propagation. In: Rumelhart D., and McClelland J. (Eds). Parallel distributed processing (1986), MIT Press, Cambridge, MA 318-362
38. Schapire R.E., Freund Y., Bartlett P.L., and Lee W.S. Boosting the margin: A new explanation for the effectiveness of voting methods. Annals of Statistics 26 5 (1998) 1651-1686
39. Schapire R.E., and Singer Y. Improved boosting algorithms using confidence-rated predictions. Machine Learning 37 (1999) 297-336
40. Skurichina, M., & Duin, R. P. W. (2001) Bagging and the random subspace method for redundant feature spaces, In J. Kittler, R. Poli (Eds.), Proceedings of the second international workshop on multiple classifier systems MCS 2001 (pp. 1-10)
41. Sun Y., Todorovic S., and Li J. Reducing the overfitting of adaboost by controlling its data distribution skewedness. International Journal of Patterns Recognition and Artificial Intelligence 20 7 (2006) 1093-1116
42. Tao D., Tang X., Li X., and Wu X. Asymmetric bagging and random subspace for support vector machines-bases relevance feedback in image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 28 7 (2006) 1088-1099
43. Tumer K., and Ghosh J. Error correlation and error reduction in ensemble classifier. Connection Science 8 3-4 (1996) 385-404
44. Webb G.I. Multiboosting: A technique for combining boosting and wagging. Machine Learning 40 2 (2000) 159-196
45. Wilcoxon F. Individual comparisons by ranking methods. Biometrics 1 (1945) 80-83