A Kernel Method for Multi-Labelled Classification

The MIT Press eBooksPublished 8 November 2002

André Elisseeff, Jason Weston

DOI10.7551/mitpress/1120.003.0092

Citations1,336

TL;DR

This article presents a Support Vector Machine like learning system to handle multi-label problems, based on a large margin ranking system that shares a lot of common properties with SVMs.

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.

Keywords

Computer ScienceBiochemistry, Genetics and Molecular Biology

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