Alina Beygelzimer, John Langford, Bianca Zadrozny
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.
Subjects: 12. Machine Learning and Discovery; 9.3 Mathematical Foundations
Content Area: 12. Machine Learning
Submitted: May 10, 2005