Weighted One-Against-All

Book One

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 20

Machine Learning

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.