Multi-class Boosting with Class Hierarchies
We propose AdaBoost.BHC, a novel multi-class boosting algorithm. AdaBoost.BHC solves a C class problem by using C − 1 binary classifiers defined by a hierarchy that is learnt on the classes based on their closeness to one another. It then applies AdaBoost to each binary classifier. The proposed algorithm is empirically evaluated with other multi-class AdaBoost algorithms using a variety of datasets. The results show that AdaBoost.BHC is consistently among the top performers, thereby providing a very reliable platform. In particular, it requires significantly less computation than AdaBoost.MH, while exhibiting better or comparable generalization power.
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- 1.Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. In: European Conference on Computational Learning Theory, pp. 23–37 (1995)Google Scholar
- 3.Abney, S., Schapire, R., Singer, Y.: Boosting applied to tagging and pp attachment (1999)Google Scholar
- 5.Schapire, R.E.: Using output codes to boost multiclass learning problems. In: ICML 1997: Proceedings of the Fourteenth International Conference on Machine Learning, pp. 313–321 (1997)Google Scholar
- 11.Zhu, J., Rosset, S., Zou, H., Hastie, T.: Multi-class adaboost. Tech. rep., Department of Statistics, University of Michigan, Ann Arbor, MI 48109 (2006)Google Scholar
- 13.Asuncion, A., Newman, D.J.: UCI machine learning repository (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
- 15.Li, L.: Multiclass boosting with repartitioning. In: ICML 2006: Proceedings of the 23rd international conference on Machine learning, pp. 569–576. ACM, New York (2006)Google Scholar