Article

A kernel method for multi-labelled classification

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NIPS'01: Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic

Pages 681 - 687

Published: 03 January 2001 Publication History

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Abstract

This article presents a Support Vector Machine (SVM) like learning system to handle multi-label problems. Such problems are usually decomposed into many two-class problems but the expressive power of such a system can be weak [5, 7]. We explore a new direct approach. It is based on a large margin ranking system that shares a lot of common properties with SVMs. We tested it on a Yeast gene functional classification problem with positive results.

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André Elisseeff and Jason Weston. Kernel methods for multi-labelled classification and categorical regression problems. Technical report, BIOwulf Technologies, 2001. http://www.bht-labs.com/public/.

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Demir KNguyen BXue BZhang MSilva SPaquete L(2023)Co-operative Co-evolutionary Many-objective Embedded Multi-label Feature Selection with Decomposition-based PSOProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590373(438-446)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590373
Giunchiglia ELukasiewicz T(2022)Multi-Label Classification Neural Networks with Hard Logical Constraints Journal of Artificial Intelligence Research10.1613/jair.1.1285072(759-818)Online publication date: 4-Jan-2022
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Nguyen VHüllermeier E(2022)Multilabel Classification with Partial AbstentionJournal of Artificial Intelligence Research10.1613/jair.1.1261072(613-665)Online publication date: 4-Jan-2022
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A kernel method for multi-labelled classification

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NIPS'01: Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic

January 2001

1594 pages

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MIT Press

Cambridge, MA, United States

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Published: 03 January 2001

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