Improving Adaptive Boosting with k-Cross-Fold Validation
As seen in the bibliography, Adaptive Boosting (Adaboost) is one of the most known methods to increase the performance of an ensemble of neural networks. We introduce a new method based on Adaboost where we have applied Cross-Validation to increase the diversity of the ensemble. We have used Cross-Validation over the whole learning set to generate an specific training set and validation set for each network of the committee. We have tested Adaboost and Crossboost with seven databases from the UCI repository. We have used the mean percentage of error reduction and the mean increase of performance to compare both methods, the results show that Crossboost performs better.
KeywordsEnsemble Method Error Reduction Single Network Wisconsin Breast Cancer Previous Network
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- 2.Raviv, Y., Intratorr, N.: Bootstrapping with Noise: An Effective Regularization Technique. Connection Science, Special issue on Combining Estimators 8, 356–372 (1996)Google Scholar
- 4.Hernandez, E.C., Torres, S.J., Fernandez, R.M.: New Experiments on Ensembles of Multilayer Feedforward for Classification Problems. In: Proceedings of International Conference on Neural Networks, IJCNN 2005, Montreal, Canada, pp. 1120–1124 (2005)Google Scholar
- 6.Freund, Y., Schapire, R.E.: Experiments with A New Boosting Algorithm. In: International Conference on Machine Learning, pp. 148–156 (1996)Google Scholar
- 11.Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J.: UCI Repository of Machine Learning Databases (1998)Google Scholar