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Multiclass Boosting with Repartitioning Ling Li ling@caltech.edu

Summary: Multiclass Boosting with Repartitioning
Ling Li ling@caltech.edu
Learning Systems Group, California Institute of Technology, Pasadena, CA 91125, USA
A multiclass classification problem can be re-
duced 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 op-
timal 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 mul-
ticlass boosting algorithm that modifies the
coding matrix according to the learning abil-
ity of the base learner. We show experimen-


Source: Abu-Mostafa, Yaser S. - Department of Mechanical Engineering & Computer Science Department, California Institute of Technology


Collections: Computer Technologies and Information Sciences