C.A. Micchelli, W.L. Miranker
Journal of the ACM
The one-against-all reduction from multiclass classification to binary classification is a standard technique used to solve multiclass problems with binary classifiers. We show that modifying this technique in order to optimize its error transformation properties results in a superior technique, both experimentally and theoretically. This algorithm can also be used to solve a more general classification problem "multi-label classification," which is the same as multiclass classification except that it allows multiple correct labels for a given example. Copyright © 2005, American Association for Artificial Intelligence (www.aaai.org). All rights reserved.
C.A. Micchelli, W.L. Miranker
Journal of the ACM
Saurabh Paul, Christos Boutsidis, et al.
JMLR
Joxan Jaffar
Journal of the ACM
Kenneth L. Clarkson, Elad Hazan, et al.
Journal of the ACM