Abstract
If one has a multiclass classification problem and wants to boost a multiclass base classifier AdaBoost.M1 is a well known and widely applicated boosting algorithm. However AdaBoost.M1 does not work, if the base classifier is too weak. We show, that with a modification of only one line of AdaBoost.M1 one can make it usable for weak base classifiers, too. The resulting classifier AdaBoost.M1Wis guaranteed to minimize an upper bound for a performance measure, called the guessing error, as long as the base classifier is better than random guessing. The usability of AdaBoost.M1W could be clearly demonstrated experimentally.
Chapter PDF
Similar content being viewed by others
References
E. L. Allwein, R. E. Schapire, Y. Singer 2000. Reducing multiclass to binary: a unifying approach for margin classifiers. Machine Learning 1, 113–141.
T. G. Dietterrich, G. Bakiri, 1995. Solving multiclass learning problems via errorcorrecting output codes. Journal of Artificial Intelligence Research 2, 263–286.
Y. Freund, R. E. Schapire, 1997. A decision-theoretic generalization of onlinelearning and an application to boosting. Journal of Computer and System Sciences 55(1), 119–139.
V. Guruswami, A. Sahai, 1999. Multiclass learning, boosting, and error-correcting codes. Proceedings of the Twelfth Annual Conference on Computational Learning Theory 145–155.
R. E. Schapire, 1997. Using output codes to boost multiclass learning problems. Machine Learning: Proceedings of the Fourteenth International Conference, 313–321.
R. E. Schapire, Y. Singer, 1999. Improved boosting algorithms using confidence-rated predictions. Machine Learning 37, 297–336.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Eibl, G., Pfeiffer, K.P. (2002). How to Make AdaBoost.M1 Work for Weak Base Classifiers by Changing Only One Line of the Code. In: Elomaa, T., Mannila, H., Toivonen, H. (eds) Machine Learning: ECML 2002. ECML 2002. Lecture Notes in Computer Science(), vol 2430. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36755-1_7
Download citation
DOI: https://doi.org/10.1007/3-540-36755-1_7
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44036-9
Online ISBN: 978-3-540-36755-0
eBook Packages: Springer Book Archive