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Multiclass boosting with repartitioning

Li, Ling (2006) Multiclass boosting with repartitioning. In: ICML '06 Proceedings of the 23rd international conference on Machine learning. ACM , New York, NY, pp. 569-576. ISBN 1-59593-383-2.

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A multiclass classification problem can be reduced to a collection of binary problems with the aid of a coding matrix. The quality of the final solution, which is an ensemble of base classifiers learned on the binary problems, is affected by both the performance of the base learner and the error-correcting ability of the coding matrix. A coding matrix with strong error-correcting ability may not be overall optimal if the binary problems are too hard for the base learner. Thus a trade-off between error-correcting and base learning should be sought. In this paper, we propose a new multiclass boosting algorithm that modifies the coding matrix according to the learning ability of the base learner. We show experimentally that our algorithm is very efficient in optimizing the multiclass margin cost, and outperforms existing multiclass algorithms such as AdaBoost.ECC and one-vs-one. The improvement is especially significant when the base learner is not very powerful.

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Additional Information:Copyright 2006 by the author(s)/owner(s). This work was supported by the Caltech SISL Graduate Fellowship.
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Caltech Social Science Experimental LaboratoryUNSPECIFIED
Record Number:CaltechAUTHORS:20161122-145403527
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Official Citation:Ling Li. 2006. Multiclass boosting with repartitioning. In Proceedings of the 23rd international conference on Machine learning (ICML '06). ACM, New York, NY, USA, 569-576. DOI=
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:72259
Deposited By: Kristin Buxton
Deposited On:22 Nov 2016 23:41
Last Modified:03 Oct 2019 16:16

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