Multiclass boosting with repartitioning
- Creators
- Li, Ling
- Others:
- Cohen, William
- Moore, Andrew
Abstract
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.
Additional Information
Copyright 2006 by the author(s)/owner(s). This work was supported by the Caltech SISL Graduate Fellowship.Attached Files
Published - p569-li.pdf
Files
Name | Size | Download all |
---|---|---|
md5:69fc316569d400a5e10f5b035b9ab753
|
429.8 kB | Preview Download |
Additional details
- Eprint ID
- 72259
- Resolver ID
- CaltechAUTHORS:20161122-145403527
- Caltech Social Science Experimental Laboratory
- Created
-
2016-11-22Created from EPrint's datestamp field
- Updated
-
2021-11-11Created from EPrint's last_modified field