N-ary decomposition for multi-class classification
- Publication Type:
- Journal Article
- Citation:
- Machine Learning, 2019, 108 (5), pp. 809 - 830
- Issue Date:
- 2019-05-15
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| N-ary decomposition for multi-class classification.pdf | Published Version | 690.53 kB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
© 2019, The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature. A common way of solving a multi-class classification problem is to decompose it into a collection of simpler two-class problems. One major disadvantage is that with such a binary decomposition scheme it may be difficult to represent subtle between-class differences in many-class classification problems due to limited choices of binary-value partitions. To overcome this challenge, we propose a new decomposition method called N-ary decomposition that decomposes the original multi-class problem into a set of simpler multi-class subproblems. We theoretically show that the proposed N-ary decomposition could be unified into the framework of error correcting output codes and give the generalization error bound of an N-ary decomposition for multi-class classification. Extensive experimental results demonstrate the state-of-the-art performance of our approach.
Please use this identifier to cite or link to this item: