Graded Multilabel Classification: The Ordinal Case

We propose a generalization of multilabel classification that we refer to as graded multilabel classification. The key idea is that, instead of requesting a yes-no answer to the question of class membership or, say, relevance of a class label for an instance, we allow for a graded membership of an instance, measured on an ordinal scale of membership degrees. This extension is motivated by practical applications in which a graded or partial class membership is natural. Apart from introducing the basic setting, we propose two general strategies for reducing graded multilabel problems to conventional (multilabel) classification problems. Moreover, we address the question of how to extend performance metrics commonly used in multilabel classification to the graded setting, and present first experimental results.

• Type: Conference
• Year: 2010
• Where: ICML
• Authors: Weiwei Cheng, Krzysztof Dembczynski, Eyke Hüllermeier
• Keywords: Class Membership, Graded, ICML 2010, Machine Learning, Multilabel Classification