Abstract
Label ranking is the task of inferring a total order over a predefined set of labels for each given instance. We present a general framework for batch learning of label ranking functions from supervised data. We assume that each instance in the training data is associated with a list of preferences over the label-set, however we do not assume that this list is either complete or consistent. This enables us to accommodate a variety of ranking problems. In contrast to the general form of the supervision, our goal is to learn a ranking function that induces a total order over the entire set of labels. Special cases of our setting are multilabel categorization and hierarchical classification. We present a general boosting-based learning algorithm for the label ranking problem and prove a lower bound on the progress of each boosting iteration. The applicability of our approach is demonstrated with a set of experiments on a large-scale text corpus.
Original language | English (US) |
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Title of host publication | Advances in Neural Information Processing Systems 16 - Proceedings of the 2003 Conference, NIPS 2003 |
Publisher | Neural information processing systems foundation |
ISBN (Print) | 0262201526, 9780262201520 |
State | Published - Jan 1 2004 |
Externally published | Yes |
Event | 17th Annual Conference on Neural Information Processing Systems, NIPS 2003 - Vancouver, BC, Canada Duration: Dec 8 2003 → Dec 13 2003 |
Other
Other | 17th Annual Conference on Neural Information Processing Systems, NIPS 2003 |
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Country/Territory | Canada |
City | Vancouver, BC |
Period | 12/8/03 → 12/13/03 |
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Information Systems
- Signal Processing