Two bagging algorithms with coupled learners to encourage diversity

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

Volumn 4723 LNCS, Issue , 2007, Pages 130-139

  Valle, Carlos ^a   Ñanculef, Ricardo ^a   Allende, Héctor ^a   Moraga, Claudio ^b,c  

^a DEPARTAMENTO DE FÍSICA   (Chile)

^b EUROPEAN CENTRE FOR SOFT COMPUTING   (Spain)

^c TU DORTMUND UNIVERSITY   (Germany)

Author keywords

Bagging; Ensemble diversity; Ensemble methods; Neural networks; Out of bag estimation; Regression estimators

Indexed keywords

LEARNING ALGORITHMS; NEURAL NETWORKS; PROBLEM SOLVING; REGRESSION ANALYSIS; ROBUSTNESS (CONTROL SYSTEMS);

ENSEMBLE DIVERSITY; ENSEMBLE LEARNING; REGRESSION ESTIMATORS;

LEARNING SYSTEMS;

EID: 38049056650     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-74825-0_12     Document Type: Conference Paper

References (17)

1. Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998)
2. Breiman, L.: Bagging predictors. Machine Learning 26(2), 123-140 (1996)
3. Breiman, L.: Out-of-bag estimation. Technical report, Statistics Department, University of California (1997)
4. Breiman, L.: Using iterated bagging to debias regressions. Machine Learning 45(3), 261-277 (2001)
5. Brown, G.: Diversity in Neural Network Ensembles. PhD thesis, School of Computer Science, University of Birmingham (2003)
6. Drucker, H., Burges, C.J.C., Kaufman, L., Smola, A., Vapnik, V.: Support vector regression machines. In: Advances in Neural Information Processing Systems, vol. 9, p. 155. The MIT Press, Cambridge (1997)
7. Efron, B., Tibshirani, R.: An Introduction to the Bootstrap. Chapman & Hall (1993)
8. Elisseeff, A., Evgeniou, T., Pontil, M.: Stability of randomized learning algorithms. J. Machine Learning Research 6, 55-79 (2005)
9. Friedman, J.: Greedy function approximation: A gradient boosting machine. Annals of Statistics 29(5) (2001)
10. Friedman, J.: Stochastic gradient boosting. Computational Statistics and Data Analysis 38(4), 367-378 (2002)
11. Grandvalet, Y.: Bagging can stabilize without reducing variance. In: Dorffner, G., Bischof, H., Hornik, K. (eds.) ICANN 2001. LNCS, vol. 2130, pp. 49-56. Springer, Heidelberg (2001)
12. Grandvalet, Y.: Bagging equalizes influence. Machine Learning 55(3), 251-270 (2004)
13. Poggio, T., Rifkin, R., Mukherjee, S.: Bagging regularizes. Technical Report 214/AI Memo 2002-003, MIT CBCL (2002)
14. Nanculef, R., Valle, C., Allende, H., Moraga, C.: Ensemble learning with local diversity. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds.) ICANN 2006. LNCS, vol. 4131, pp. 264-273. Springer, Heidelberg (2006)
15. Suen, Y., Melville, P., Mooney, R.: Combining bias and variance reduction techniques for regression. In: Proceedings of the 16th European Conference on Machine Learning, pp. 741-749 (2005)
16. Vlachos, P.: StatLib datasets archive (2005)
17. Yao, X., Lui, Y.: Ensemble learning via negative correlation. Neural Networks 12(10), 1399-1404 (1999)