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Eastwood, Mark; Gabrys, Bogdan (2008)
Publisher: EXIT Publishing House
Languages: English
Types: Part of book or chapter of book
Subjects: aintel, csi
This chapter covers different approaches that may be taken when building an\ud ensemble method, through studying specific examples of each approach from research\ud conducted by the authors. A method called Negative Correlation Learning illustrates a\ud decision level combination approach with individual classifiers trained co-operatively. The\ud Model level combination paradigm is illustrated via a tree combination method. Finally,\ud another variant of the decision level paradigm, with individuals trained independently\ud instead of co-operatively, is discussed as applied to churn prediction in the\ud telecommunications industry.
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    • 1. Bousquet, O., S. Boucheron, and G. Lugosi, Introduction to Statistical Learning Theory, in Advanced Lectures on Machine Learning, U.v.L. Bousquet O. and G. R├Ątsch, Editors. 2004, Springer: Heidelberg, Germany. p. 169-207.
    • 2. Brown, G., et al., Diversity creation methods: A survey and categorisation. Journal of Information Fusion, 2005. 6(1).
    • 3. Kuncheva, L.I., Combining Pattern Classifiers: Methods and Algorithms. 2004: WileyInterscience.
    • 4. Opitz, D. and R. Maclin, Popular Ensemble Methods: An Empirical Study. Journal of Artificial Intelligence Research, 1999. 11: p. 169-198.
    • 5. Hansen, J.V., Combining Predictors: Comparison of Five Meta Machine Learning Methods. Information Sciences, 1999. 119(1-2): p. 91-105.
    • 6. Breiman, L., Bagging Predictors. Machine Learning, 1996. 24(2): p. 123-140.
    • 7. Freund, Y. and R.E. Schapire. Experiments with a new boosting algorithm. in Proceedings of the 13th International Conference on Machine Learning. 1996: Morgan Kaufmann.
    • 8. Breiman, L., Random Forests. Machine Learning, 2001. 45(1): p. 5-32.
    • 9. Hall, M., Combining Particles and Waves for Fluid Animation. 1998. p. 73.
    • 10. Domingos, P., Knowledge discovery via multiple models. 1998.
    • 11. Gert Lanckriet, N.C., Peter Bartlett, Laurent El Ghaoui, Michael Jordan,, Learning the Kernel Matrix with Semi-Definite Programming.
    • 12. Lee, S.W., S. Verzakov, and R.P. Duin. Kernel Combination Versus Classifier Combination. in Proc. 7th Int. WOrkshop, MCS 2007. 2007.
    • 13. Gabrys, B., Learning Hybrid Neuro-Fuzzy Classifier Models From Data: To Combine or not ot Combine? Fuzzy Sets and Systems, 2004. 147: p. 39-56.
    • 14. Utans, J., Weight averaging for neural networks and local resampling schemes. 1996.
    • 15. Geman, S., E. Bienenstock, and R. Doursat, Neural Networks and the Bias/Variance Dilemma. Neural Computation, 1992. 4(1): p. 1-58.
    • 16. Tumer, K. and J. Ghosh, Error Correlation and Error Reduction in Ensemble Classifiers. Connection Science, 1996. 8(3-4): p. 385-403.
    • 17. Tumer, K. and J. Ghosh, Analysis of decision boundaries in linearly combined neural classifiers. Pattern Recognition, 1996. 29(2): p. 341-348.
    • 18. Kohavi, R. and D.H. Wolpert. Bias Plus Variance Decomposition for Zero-One Loss Functions. in Machine Learning: Proceedings of the Thirteenth International Conference. 1996: Morgan Kaufmann.
    • 19. Breiman, L., Bias, Variance, and Arcing Classifiers. Breiman,L. (1996) Bias, Variance, and Arcing Classifiers, Technical Report 460, Statistics Department, University of California, 1996.
    • 20. Domingos, P. A Unified Bias-Variance Decomposition and its Applications. in Proc. 17th International Conf. on Machine Learning. 2000: Morgan Kaufmann, San Francisco, CA.
    • 21. Krogh, A. and J. Vedelsby, Neural Network Ensembles, Cross Validation, and Active Learning. NIPS, 1995. 7: p. 231-238.
    • 22. Liu, Y. and X. Yao, Ensemble learning via negative correlation. Neural Networks, 1999. 12: p. 1399-1404.
    • 23. Brown, G. and J.L. Wyatt. The Use of the Ambiguity Decomposition in Neural Network Ensemble Learning Methods. in 20th International Conference on Machine Learning (ICML'03). 2003. Washington DC, USA.
    • 24. McKay, R. and H. Abbass. Analyzing Anticorrelation in Ensemble Learning. in Proceedings of 2001 Conference on Artificial Neural Networks and Expert Systems. 2001. Otago, New Zealand.
    • 25. Islam, M.M., X. Yao, and K. Murase, A constructive algorithm for training cooperative neural network ensembles. IEEE Transactions on Neural Networks, 2003. 14(4): p. 820-834.
    • 26. Eastwood, M. and B. Gabrys, The Dynamics of Negative Correlation Learning. Journal of VLSI Signal Processing, 2007. 49: p. 251-263.
    • 27. Quinlan, J.R., Simplifying Decision Trees, in Knowledge Acquisition for KnowledgeBased Systems, B. Gaines and J. Boose, Editors. 1988, Academic Press: London. p. 239- 252.
    • 28. Kearns, M. and Y. Mansour. A fast, bottom-up decision tree pruning algorithm with near-optimal generalization. in Proc. 15th International Conf. on Machine Learning. 1998:
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