Some fundamental issues in ensemble methods

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2008, Pages 2243-2250

  Wang, Wenjia ^a  

^a UNIVERSITY OF EAST ANGLIA   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARKING; ELECTRIC NETWORK ANALYSIS; LEARNING ALGORITHMS; LEARNING SYSTEMS; PROBLEM SOLVING;

APPLICATIONS.; BENCHMARK DATUMS; ENSEMBLE METHODS; INDIVIDUAL MODELS; MACHINE LEARNINGS; THEORETICAL ANALYSES;

NEURAL NETWORKS;

EID: 56349084721     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2008.4634108     Document Type: Conference Paper

References (17)

1. Geman, S. et al. (1992): Neural networks and the bias/variance dilemma. Neural computation, vol.4, pp1-58.
2. Breiman, L.(1996): Bagging prediction. Machine learning, 24, pp.123-140.
3. th Int. Conference, pp322-330, Morgan Kaufmann.
4. Freund, Y. & Schapire R.E. (1996): Experiments with a new boosting algorithm, in L. Saitta, ed., Machine Learning: Proceedings of the 13th national conference, Morgan Kaufmann. pp148-156.
5. Wang, W. & Partridge, D. (1998): Multi-version neural network systems. Proceedings of neural networks and their applications, NEURAP'98, Marseilles, France, 1998, pp351-357.
6. Wang, W. et al.. (2001): Hybrid Ensembles and Coincident-Failure Diversity, in Proceedings of IJCNN01, pp2376-2381.
7. Breiman L. (2001): Random Forests. Machine Learning 45(1): pp 5-32.
8. Hansen, L. et al. (1990): Neural network ensembles. IEEE Trans. Patterns Analysis and Machine Intelligence, vol. 12 pp993-1001.
9. Optiz, D. & Maclin, R. (1999): Popular Ensemble Methods: An Empirical Study. J of Artificial Intelligence Research, 11. pp169-198.
10. Partridge, D. et al. (1996): Engineering multi-version neural-net systems. Neural Computation, vol. 8, pp869-893.
11. Wang W. et al (2000): Diversity between Neural Networks and Decision Trees for Building Multiple Classifier Systems. Multiple Classifier Systems: pp 240-249.
12. Richards, G. & Wang, W. (2006): Empirical Investigations on Characteristics of Ensemble and Diversity. UCNN06, July 2006.
13. Kuncheva, L..(2003): Measures of diversity in classifier ensembles and their relationships with the ensemble accuracy. Kluwer Academic Publisher, Netherlands, 2003.
14. Bian, S. & Wang, W. (2007): On diversity and accuracy of homogeneous and heterogeneous ensembles. Int. Journal of Hybrid Intelligent Systems, Vol. 4, No.2, pp103-128.
15. Wang, W. et al., (2001): Hybrid Ensembles and Coincident-Failure Diversity, in Proceedings of UCNN01, pp2376-2381.
16. Wang, W. et al. (2005). Hybrid Data Mining Ensemble for Predicting Osteoporosis Risk. IEEE-EMBS 2005. 27th Int. Conf. on Engineering in Medicine and Biology, pp886 - 889.
17. Grove, A. and Schuurmans, D. (1998) Boosting in the limit: Maximizing the margin of learned ensembles. In Proc. of the 15th Nat. Conf. on Artificial Intelligence (AAAI-98).