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.
REF adopted a Support Vector Machine (SVM) like learning system to handle multi-label problem.
A kernel method for multi-labelled classification
NIPS
Computer Science